Interview guides · Avid Bioservices

Avid Bioservices Software Engineer Interview Guide

Avid Bioservices Software Engineer interview guide: HR screen and panel rounds, validation, troubleshooting and CGMP questions, and a 7-day plan.

The 60-second brief

Avid Bioservices is a contract development and manufacturing organization (CDMO) that develops and manufactures biopharmaceutical products under CGMP. Because it manufactures for clients, the software around its production floor has to be reliable, traceable and auditable. Candidates describe Software Engineer openings in different tracks: Manufacturing Science and Technology (MSAT, with upstream and downstream variants), Operational Technology (OT), or IT Systems Compliance. The work reported across them includes keeping manufacturing software running and upgraded, making sure data historians capture batch data correctly, resolving software deviations, configuring software parameters when a process is introduced or scaled up, supporting validation, and writing or revising SOPs.

This guide is for candidates interviewing for a Software Engineer position at Avid Bioservices, whichever track the opening belongs to (MSAT, OT or IT Systems Compliance). It covers the two reported rounds, the fifteen reported questions and how to answer them, the regulated-environment vocabulary worth learning first (CGMP, SOPs, SDLC, IQ/OQ/PQ, 21 CFR Part 11), and a seven-day plan. Candidates describe the loop as practical and conversational rather than algorithm-heavy, so the guide spends its time on troubleshooting method, validation, documentation and working with non-software colleagues.

Candidates report two stages over roughly 2-4 weeks: an HR Screening, then a Core Interview Stage made up of a panel or back-to-back sessions with several groups, typically three teams for about 30 minutes each.

Focus areas

MSAT (Manufacturing Science & Technology)
IT Systems Compliance
Software Engineering (General)

Overview

The reported questions fall into three groups: technical and systems troubleshooting (a crashing application, integrating a tool into a legacy network, historians and control systems, round-the-clock availability, performance under constraints), compliance and quality (software validation and the SDLC, SOP conformance, audit documentation, a colleague who wants to skip a compliance step, data integrity), and behavioral questions (cross-functional delivery, urgent tickets, a mistake, why a CDMO, speed versus documentation). Nothing reported is a whiteboard algorithm problem, so the preparation is about explaining real work clearly.

The systems that candidates mention set the vocabulary you need: PLCs, SCADA and DCS on the control side, data historians and MES for recorded batch data, and IQ/OQ/PQ, change control and 21 CFR Part 11 (electronic signatures and audit trails) on the compliance side. Batch control standards such as ISA-88 and automation platforms such as DeltaV are described as less common topics. You do not need to be an expert in all of them, but you should be able to say what each is for and where your own experience touches it.

Because the panel stage spans several teams, the same story may be heard by an automation group, a quality group and a process group. Prepare each story with a technical layer and a plain-language layer, and write down the record that existed afterwards: the ticket, the test evidence, the change request, the SOP revision. In a regulated plant, how you document a change is part of the answer, and the strongest stories show the record as well as the fix.

The Avid Bioservices interview process

Candidates describe two stages: a screening call with HR, then a core stage that is either a panel or several back-to-back sessions with different groups. The exact technical stakeholders depend on whether the opening is MSAT, OT or IT Systems Compliance.

HR Screening

Candidates report that an HR representative runs the first stage and covers your high-level technical background and cultural fit. Treat it as the first pass over your story rather than a technical exam: you will need a short, accurate account of your systems, automation or compliance work and a reason for choosing a CDMO. Candidates describe the surrounding environment as formal and punctual, so be on time and dressed for a business setting, whether the call is virtual or on site.

What to demonstrate

High-level technical background, as candidates report it
Cultural fit with a formal but friendly workplace
Whether your experience maps to the MSAT, OT or IT Systems Compliance track on offer

How to prepare

Write a two-minute career summary that names the systems you have run (servers, networks, control or data systems), what you documented, and any regulated setting you worked in.
Rehearse your answer to "Why are you interested in working for a CDMO like Avid Bioservices?" using the contract-manufacturing model, not a generic line about biotech.
Learn what SOP, SDLC, CGMP and IQ/OQ/PQ stand for so the vocabulary is familiar even if you have not used it professionally.
Ask which track the opening belongs to (MSAT, OT or IT Systems Compliance) so you can pick the right stories for the next stage.

Core Interview Stage

Candidates report a panel or a series of back-to-back sessions with several groups, typically three teams for about 30 minutes each. Candidates also report that the stakeholders depend on the track, and that the conversation covers technical capability, compliance understanding and how you work with other departments. Since the questions are conversational, a structured answer matters more than speed. For troubleshooting, state your sequence; for compliance, name the record; for people questions, use situation, action, result and what changed afterwards.

What to demonstrate

Hands-on troubleshooting method for software and systems that support manufacturing
Understanding of validation, change control, documentation and data integrity
Ability to talk with quality, operations and process colleagues who are not software specialists

How to prepare

Prepare one story each for control systems or data historians, software validation, audit documentation and a compliance disagreement, and say each aloud in a few minutes.
Practise the troubleshooting question as a sequence: confirm the symptom, check what changed, contain the impact, isolate the cause, fix, verify, record.
Build a one-page answer on data integrity covering accurate, attributable, traceable records, audit trails and access control, then link it to a system you have worked on.
Prepare plain-language explanations of one technical limitation and one technical trade-off, as if speaking to a manufacturing operator or quality reviewer.

Where candidates lose points

Treating validation and documentation as paperwork you did afterwards

Present the records as part of the work: requirement, test evidence, approval, change request. Rehearse the validation story and the audit-documentation story with the artifacts named. Candidates report that testing, validation and documentation count as much as building the system.

Answering the compliance-bypass question by giving in, or by lecturing

Find the pressure behind the request, offer a faster compliant path (expedited review, parallel tests, emergency change with approval afterwards), and escalate to the owner if it persists. Practise the answer with a real example, not a principle.

Claiming PLC, SCADA or historian experience you cannot detail

Describe only systems you touched, and for the rest name the closest equivalent and what transfers. A specific honest answer to the data-historian question beats a broad claim that falls apart on the first follow-up.

Troubleshooting as a list of guesses with no containment or record

For the crash and communication-failure scenarios, say the order out loud: confirm the symptom, check what changed, limit the impact on running work, isolate, fix, verify, and record the cause and action.

Giving a reason for wanting this CDMO that fits any employer

Say what the contract-manufacturing model means for your work (client products, traceable records, validated systems) and connect it to one specific experience. Skip praise for the company and the culture.

Questions & practice

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.

Approach

Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.

Follow-up

The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
How do you verify the backfill actually finished, given rows are still being inserted while it runs?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

How do you approach integrating a new software tool into an existing legacy network?

Medium · Technical and Systems Troubleshooting

Approach

Start with an inventory: what the legacy side runs (OS version, vendor support status), which protocols it speaks (OPC DA, OPC UA, Modbus, ODBC, file drops), which systems are validated, and whether the new tool only needs to read or must also write back.
Respect network segmentation. Keep plant-floor and business networks separate (the Purdue/ISA-95 layering), put the integration point in a DMZ or behind a gateway, open only the specific ports and hosts it needs, and never bridge IT straight to control-level devices.
Prefer an adapter over changes to the legacy system. For example, an OPC UA gateway avoids classic OPC DA's DCOM problems across firewalls. Use store-and-forward buffering so a link outage delays data rather than losing it, and key writes by tag plus timestamp so retries cannot create duplicates.
Roll out in stages: test environment first, then a parallel run that compares the new tool's values with the existing path, then cutover in a planned window with a written rollback plan. In a GxP context, the change goes through change control and validation testing (IQ/OQ/PQ).
Settle day-two ownership up front: service accounts and access reviews, who patches the gateway, monitoring of the interface itself, and what happens to data if the tool is later retired.

Follow-up

The legacy host runs an unsupported OS that cannot be patched. How do you connect to it safely?
How would you prove the new tool reads exactly the same values as the old path?
What is your rollback if the integration slows down the legacy system?

How do you ensure high availability for systems that run 24/7?

Medium · Technical and Systems Troubleshooting

Approach

Turn 24/7 into numbers. 99.9% availability allows about 8.8 hours of downtime a year and 99.99% about 53 minutes. Set an RTO (how long recovery may take) and an RPO (how much data may be lost) for each system, because a data historian and a reporting server need different targets.
Remove single points of failure along the whole path: clustered or virtualised hosts with HA failover, redundant network paths and switches, UPS-backed power, database replication, and redundant controller pairs where the process needs them. Components in series multiply, so two 99% components in series give about 98%.
Choose replication deliberately. Synchronous replication gives an RPO near zero but adds write latency and couples the two sites. Asynchronous replication is faster but can lose the last few seconds on failover.
Buffer at the edge. Collectors or controllers that store and forward locally keep recording through a central server outage, then backfill when it returns.
Plan maintenance without downtime: rolling patches across redundant nodes, failover tests run on a schedule rather than assumed, backups proven by actual restores, and monitoring with heartbeat alerts plus a written recovery runbook.

Follow-up

How do you patch a server in a validated system that cannot go down?
Failover has only ever worked in testing. How would you build confidence that it works in production?
Would you replicate batch records synchronously or asynchronously, and why?

How do you ensure that your code or system configurations remain compliant with internal SOPs?

Medium · Compliance and Quality

Approach

Turn each SOP requirement into a concrete control. Keep code and configuration exports in version control, require peer review before a merge or deployment, and reference the approved change request in every commit or change record.
Keep an approved configuration baseline and detect drift automatically. Compare running configuration with the baseline using checksums or a configuration-management tool, alert on differences, and review them on a schedule.
Lock down access: individual accounts with no shared admin logins, role-based least privilege, and audit trails turned on that users cannot edit or disable. 21 CFR Part 11 is the regulation on electronic records, signatures and audit trails.
Follow the change-control lifecycle: request, impact and risk assessment, testing outside production, approval, implementation, verification, then documentation. Emergency changes follow the SOP's emergency path and are documented and reviewed after the fact, never skipped.
Keep evidence a reviewer can follow: test records, approvals and training records for SOP revisions. When an SOP no longer matches how the system works, raise a revision instead of quietly working around it.

Follow-up

How would you detect a configuration change someone made directly on a production server without a change request?
An urgent security patch is needed overnight on a validated system. What does the change record look like?
What do you do when an SOP step no longer matches how the system actually behaves?

Explain your understanding of data integrity in a manufacturing setting.

Medium · Compliance and Quality

Approach

Define it with ALCOA+: data must be Attributable, Legible, Contemporaneous, Original and Accurate, plus Complete, Consistent, Enduring and Available. Then give a manufacturing example for a few of the letters, such as a sensor value tied to its batch, its time and the person who acknowledged it.
Name the technical controls: unique user IDs, computer-generated time-stamped audit trails that users cannot edit or switch off, clocks synchronised with NTP so timestamps across controller, historian and MES line up, and electronic signatures under 21 CFR Part 11.
Follow the data path end to end: sensor, controller, historian, report. Historian compression or deadband settings can discard values, so they need justifying. Interfaces need buffering for outages and a check that the value stored matches the value at the source.
Cover the whole lifecycle: backups proven by restores, archives kept readable for the full retention period, metadata stored with the data, and audit-trail review as part of batch record review.
Name the common failures to guard against: shared logins, deleting or rerunning results, unsynchronised clocks, critical calculations in uncontrolled spreadsheets, and gaps in data recorded without explanation.

Follow-up

The historian shows a gap in the middle of a batch. How do you handle it in the record?
How would you prove that a data migration preserved integrity?
Why does clock synchronisation matter for an audit trail?

Walk me through your troubleshooting process when a critical piece of software crashes.

Medium · Technical and Systems Troubleshooting

Approach

Contain the impact first. Is a running batch or process affected? Move to the documented fallback (redundant node, manual or paper recording), tell operations and QA, and open an incident or deviation record with timestamps from the first minute.
Preserve evidence before restarting anything. Capture the crash dump, stack trace or exit code, the application and OS event logs and the exact crash time. A restart can rotate logs and wipe in-memory state, so the cause becomes unrecoverable.
Ask what changed: patches, configuration edits in the change-control log, certificates, disk space, network or firewall changes, license servers. Then line the crash time up against those events and against the dependencies (database, historian or OPC connection, file shares).
Isolate by comparison. Check resource exhaustion (full disk, memory or handle leak). Compare the failing node with a known-good node or the test environment, then reproduce in non-production before calling something the root cause rather than just the trigger.
Close it properly. Put the fix through change control with testing, check whether any data was lost while the system was down, and record the root cause plus a corrective action, such as an alert on the resource that ran out.

Follow-up

Operators want the application restarted now, but a restart destroys the evidence. What do you do?
The crash happens roughly once a week with no visible pattern. How would you catch it in the act?
How do you confirm that no batch data was lost or corrupted while the software was down?

Explain a time you had to optimize a system's performance under tight constraints.

Medium · Technical and Systems Troubleshooting

Approach

Open with the constraint and a baseline number: fixed hardware, no downtime allowed, a validated system where every change costs requalification, or a small budget. Then give the metric you were moving (query time, CPU, historian write rate, report runtime) and its value before you touched anything.
Show that you measured before changing anything: a profiler, the database query plan (EXPLAIN), CPU, I/O wait, memory and network counters. Name the bottleneck you found. By Amdahl's law, making a step that takes 10% of the time infinitely fast saves at most about 10% overall, so the biggest share is where to start.
List the options you weighed and why you picked one: an index, caching, batched writes, archiving old data, or a longer polling interval or deadband on noisy tags. Under tight constraints, the change with the smallest footprint and the least retesting often wins.
Prove the result with a before-and-after measurement under the same load. Run regression tests and record the change through change control, so the improvement is traceable.
Say which trade-off you accepted, for example a little staleness or extra storage, and what you would do with more time or hardware.

Follow-up

How did you know the improvement came from your change and not from a lighter load that day?
What did you rule out first, and what evidence ruled it out?
What would you have done if the fix required changing a validated system?

Suggested practice checklist

Seven days built around the two reported rounds: day 1 maps the loop and vocabulary, days 2 to 5 cover the troubleshooting, validation, documentation and systems questions, and days 6 and 7 rehearse the people questions and a full panel run.

Day 1 — Map the loop and learn the vocabulary

Write out the two reported stages (HR Screening, then a panel or back-to-back sessions with about three teams) and note which track you are interviewing for: MSAT, OT or IT Systems Compliance.
Make a glossary of CGMP, SOP, SDLC, IQ/OQ/PQ, change control, 21 CFR Part 11, PLC, SCADA, DCS, MES and data historian, with a one-line meaning and where you have met each.
Write your two-minute career summary and your answer for working at a CDMO.

Deliverable: A one-page loop map, a glossary with your own experience beside each term, and a written career summary.

Day 2 — Control systems, historians and crashes

Write a structured answer for your experience with data historians or automated control systems, naming only systems you have used and the closest equivalent where you have not.
Write the troubleshooting sequence for a crashing application and for a controller that stops reaching the central historian, listing the first three checks for each.
Say both answers aloud and cut anything you could not defend under a follow-up.

Deliverable: Two written answers (experience story and troubleshooting sequence) plus a spoken recording of each.

Day 3 — Validation, SOPs and data integrity

Outline the validation story for a system you changed or released: requirements, risk, test scripts, evidence, deviations, approval.
Write how you keep configurations and code consistent with an SOP, including who reviews and how drift is found.
Write a data-integrity answer covering accurate, attributable and traceable records, audit trails and access control, tied to one system.

Deliverable: A validation story outline, an SOP-conformance answer and a data-integrity answer, each one page or less.

Day 4 — Audit documents and compliance pushback

Write the audit-documentation story with the artifacts named (system description, test evidence, change tickets) and what the audit asked afterwards.
Write the answer for a colleague who wants to bypass a compliance step, including the compliant fast path and when you escalate.
Say how you would handle an emergency patch on a validated system: contain, approve, record, then review afterwards.

Deliverable: An audit-documentation story, a pushback answer and a short emergency-change outline.

Day 5 — Integration, availability and performance

Outline how you would add a new software tool to an existing legacy network: inventory, isolation, test, rollback, documentation.
Write what availability means for systems that run 24/7: redundancy, monitoring, maintenance windows and a recovery plan.
Prepare one performance-optimisation story, with the measurement before and after.

Deliverable: An integration outline, an availability answer and a measured performance story.

Day 6 — People, tickets and mistakes

Write the cross-functional project story, including the quality or process colleague and one disagreement.
Write the triage rule you use for multiple urgent tickets and a real example with the order you chose.
Write the mistake story with disclosure, containment, cause and the lasting change.

Deliverable: Three written stories with a one-line result each, ready for the conversational stage.

Day 7 — Full panel rehearsal

Run three consecutive sessions of about 30 minutes each, one on troubleshooting, one on validation and documentation, and one on behavioral questions, with a friend or a recording.
Answer the balance between rapid problem-solving and thorough documentation, and the reason for wanting a CDMO, in under two minutes each.
Review the recordings and cut filler, vague claims and any system you cannot discuss in detail.

Deliverable: Three recorded sessions, a list of weak answers to fix and a final one-page story sheet.

Behavioural preparation

Candidates report that the people-focused questions come up across the stages and sit next to the technical ones. For this role, the stories need to show work in a regulated setting: collaboration with quality, manufacturing and process colleagues, calm prioritisation, honest handling of mistakes, and respect for documentation.

Describe your experience working with data historians or automated control systems.

Medium · Technical and Systems Troubleshooting

Approach

Open with the scope in one or two sentences: which system (PLC, SCADA, DCS, historian or similar), what it controlled or recorded, and your role (operating, configuring, integrating or troubleshooting it). Only describe systems you have actually touched.
Walk through one concrete task end to end, such as adding and verifying a data point, checking collection intervals, handling a network drop so no values were lost, or backfilling a gap, and say how you confirmed the recorded value matched the source.
Bring in data trust: timestamps and clock sync, units, who could change configuration, and how changes were recorded. This links the story to data integrity.
If you lack direct historian or control-system experience, say so plainly and name the closest real equivalent (instrument or lab data logging, a time-series metrics pipeline, monitoring of production servers), what transfers, and what you would learn first. Claiming a tool you have not used is the failure that sinks this answer.
Finish with a result and a lesson: what improved or what failure you prevented.

Follow-up

What happens to the data when the link between a controller and the historian drops, and how do you recover it?
How do you confirm that a value stored in the historian matches what the instrument actually measured?
Who was allowed to change a point's configuration, and how was that change approved and recorded?

What is your experience with software validation and the SDLC in a regulated environment?

Medium · Compliance and Quality

Approach

Name the regulated setting or the closest one you have (medical, financial, aerospace, a change-controlled production system) and the lifecycle you followed: requirements, risk assessment, plan, test scripts, execution, deviations, summary and release.
Describe the testing evidence: pre-approved scripts with expected results, executed with signed and dated outcomes, and IQ/OQ/PQ (installation, operational and performance qualification) if you have used them. Explain what each one proves.
Mention traceability from each requirement to the test that covers it, and what you did when a test failed: record the deviation, assess the impact, fix, then retest instead of editing the script afterwards.
If your experience is limited to ordinary agile or CI/CD, say so and map it honestly: reviews and automated tests give you part of the evidence, while approvals, signed records and change control are the gap you would fill. Mention 21 CFR Part 11 (electronic signatures, audit trails) only as far as you understand it.
Keep the answer tied to one validated system or release so it reads as experience rather than a glossary.

Follow-up

How would you validate a small configuration change versus a new system?
What do you do when a test fails during qualification?
How does validation differ from ordinary software testing?

Describe a time you had to write technical documentation for a system audit.

Medium · Compliance and Quality

Approach

Pick one real audit, inspection or review, and state what the document had to prove and who would read it, for example a system description, a configuration baseline or an access-control record.
Describe how you built it: collecting evidence from the system itself (screenshots, logs, change tickets, test results), agreeing on the template with quality, and having it reviewed and approved before the audit.
Show the habits that make documents defensible: versioning, dated and attributable entries, traceability from requirement to test, and recording gaps honestly instead of hiding them.
State what happened afterwards, such as an auditor asking a follow-up question you could answer from the record, or a gap you corrected.
The mistake that sinks it is describing a document written from memory after the fact. Say how you kept the record current as the work happened.

Follow-up

How did you decide how much detail the document needed?
What did you do when you found the system did not match its documentation?
How did you keep the document correct after the next change?

How do you handle pushback from a team member who wants to bypass a compliance step to save time?

Medium · Compliance and Quality

Approach

Start from the principle: a required step exists for a reason (evidence, traceability, protecting a validated state), so you do not skip it quietly and you do not lecture either. Use a real example of a quality review, approval or test that someone wanted to shortcut.
First find the actual pressure: a deadline, a production issue, a step that really is redundant. Say what you asked and heard.
Offer a compliant faster route: pre-fill the change request, run the minimum required tests in parallel, ask for an expedited review, or use the emergency change path if the process has one, with the paperwork completed and approved afterwards.
If the person still wants to proceed, escalate to the supervisor or quality owner and record what was decided. Do not do the bypass yourself, and do not go around your colleague without telling them.
Close with the outcome and the relationship: the step was kept or formally waived by the right owner, and you stayed on good terms.

Follow-up

What if the production line is down and the schedule pressure is real?
How would you handle it if the person asking were more senior than you?
Have you ever agreed that a step could be skipped, and who approved that?

Tell me about a time you successfully collaborated with a cross-functional team to deliver a project.

Medium · Behavioral and Culture Fit

Approach

Choose a project with at least two non-software groups, such as quality, manufacturing operators or process engineers, and open with the shared goal and your own role in one or two sentences.
Show how you worked across the language gap: how you gathered requirements from people who think in processes rather than software, how you confirmed you understood them, and how you explained constraints in their terms.
Name one real disagreement or conflicting priority (a schedule against a review, a feature against a validation cost) and how you resolved it with evidence or a clear trade-off.
Say what you delivered and how the other groups used it, for example a tool they adopted, a test they signed off, or an SOP they updated.
The mistake that sinks it is a story where the other teams only approved your work. Show what changed because of their input.

Follow-up

How did you handle the person who was hardest to align?
What did you learn about another team's constraints that changed your design?
How did you keep everyone informed as the plan changed?

How do you manage your time when dealing with multiple urgent support tickets?

Medium · Behavioral and Culture Fit

Approach

Give a triage rule before the story: rank by impact (safety, product or batch affected, a production line stopped), then by how many users are blocked, then by age of the request. Say that the rule is explicit and written down.
Describe how you communicate: acknowledge each requester, give an honest next-update time, and tell the lower-priority requesters what is ahead of them instead of leaving them guessing.
Use a real example of two or three urgent requests arriving together, what you did first, what you handed off or escalated, and what you deferred.
Mention the record: each ticket carries its cause, action and closure so the trail exists, even for a fast fix.
Avoid answering that you work faster or stay late. Show the prioritisation and the stakeholder conversation.

Follow-up

What do you do when two supervisors both say their request is the top priority?
How do you handle a request that feels urgent to the requester but is low impact?
How do you stop the same ticket from coming back?

Describe a situation where you made a mistake on the job. How did you handle it?

Medium · Behavioral and Culture Fit

Approach

Pick a real mistake with consequences you can state, not a disguised strength. In a regulated setting, a mistake with a record behind it (a wrong configuration, a missed step, a deployment or change made without full approval) is credible.
Say how you found it and how fast you disclosed it, and to whom. Telling the right people straight away, before they found out, is the core of the story.
Describe containment first (what you stopped or rolled back), then cause, then the correction, and how you documented it, such as an incident or deviation report.
Finish with the lasting change: a checklist, a review step, an automated check or a template, and evidence that it worked.
The mistake that sinks it is blaming a tool or a colleague, or a story with no change afterwards.

Follow-up

How did the people affected react, and what did you say to them?
What would you do differently the first time you noticed the problem?
What control now prevents the same mistake?

Why are you interested in working for a CDMO like Avid Bioservices?

Medium · Behavioral and Culture Fit

Approach

Anchor the answer in what the CDMO model is: the company develops and manufactures products for clients, so work is project-driven and client-facing, and quality records matter to the customer as well as to regulators. Do not repeat marketing slogans; say it in your own words.
Connect it to your own work: software that supports production or lab operations, regulated or audited systems, and the fact that your output has a physical result.
Explain why the track fits you (MSAT, OT or IT Systems Compliance) by referring to a concrete experience, such as an automation or data task you enjoyed or a documentation habit you built.
Say what you want to learn here, such as validation practice or control-system integration, and be honest about your gaps.
Do not claim facts about the company you cannot back up. Interest in the model is stronger than praise.

Follow-up

What do you know about how a CDMO differs from a company that makes its own products?
Which part of the role do you expect to be new to you?
Why this track rather than a conventional software role?

How do you balance the need for rapid problem-solving with the requirement for thorough documentation?

Medium · Behavioral and Culture Fit

Approach

State your position first: speed and documentation do not compete when documentation happens as you work. Then give a real case, such as a production issue where you fixed fast and recorded as you went.
Describe the working method: keep a running log or ticket during diagnosis, record each action and result, use templates for change records and test evidence so writing is quick, and complete the formal write-up while details are fresh.
Separate emergency from routine: for a real emergency, take the minimum safe action, notify the owner, and complete the approval and records straight after. For routine work, no shortcuts.
Explain what you protect when time is short: the record of who changed what and when, because it cannot be recreated later.
The weak answer is that you document later when there is time. Show how the habit works under pressure.

Follow-up

What do you do when the incident ends and the write-up feels like a low priority?
How much documentation is enough for a minor configuration change?
Describe a time speed won out and how you recovered the paperwork.