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AI Competence Center Software Engineer Interview Guide

AI Competence Center Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at AI Competence Center serves as a vital technical architect and builder, responsible for developing the scalable solutions that power our digital infrastructure. You will work at the intersection of complex problem-solving and product delivery, contributing to high-impact projects that range from cloud-native applications to sophisticated AI-driven integrations. Your work directly influences how we support our clients and maintain our competitive edge in the global digital services market. This role is inherently collaborative, requiring you to bridge the gap between abstract requirements and production-ready code. You will often work alongside cross-functional teams, including product managers, cloud architects, and data specialists, to translate business needs into robust, maintainable systems.

This guide is scoped to a Software Engineer candidate at AI Competence Center.

Focus areas

Data Structures & Algorithms (DSA)
SQL / Relational Databases
AWS (Amazon Web Services)

The AI Competence Center interview process

AI Competence Center candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

The first step involves a preliminary assessment to evaluate the candidate's fit for the role.

What to demonstrate

The first step involves a preliminary assessment to evaluate the candidate's fit for the role
Depth in Data Structures & Algorithms (DSA)

How to prepare

Be able to walk your CV end to end in two minutes, and say why this company specifically.
Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Technical Assessments

Candidates undergo a series of technical evaluations to assess their core technical capabilities.

What to demonstrate

Candidates undergo a series of technical evaluations to assess their core technical capabilities
Depth in Data Structures & Algorithms (DSA)

How to prepare

Answer aloud and timed: How would you describe your experience with Java, Spring Boot, AWS, or SQL?
Answer aloud and timed: What are the common pitfalls of callback hell in Node.js, and how do you avoid them?

Managerial Assessments

This stage focuses on evaluating the candidate's ability to thrive in a collaborative, client-facing environment.

What to demonstrate

This stage focuses on evaluating the candidate's ability to thrive in a collaborative, client-facing environment
Depth in Data Structures & Algorithms (DSA)

How to prepare

Answer aloud and timed: Can you explain the difference between React and Vue.js and when you would prefer one over the other?
Answer aloud and timed: How would you go about building a new software application from scratch?

Cross-Functional Meetings

Candidates meet with team members from various departments to reflect the company's cross-functional alignment.

What to demonstrate

Candidates meet with team members from various departments to reflect the company's cross-functional alignment
Depth in Data Structures & Algorithms (DSA)

How to prepare

Answer aloud and timed: How would you resolve a complex queueing process or data flow bottleneck?
Answer aloud and timed: How does Kubernetes fit into your deployment workflow?

Final Offer

The process concludes with a discussion of the final offer, if the candidate successfully passes all previous stages.

What to demonstrate

The process concludes with a discussion of the final offer, if the candidate successfully passes all previous stages
Depth in Data Structures & Algorithms (DSA)

How to prepare

Answer aloud and timed: How would you describe the internet to someone entirely unfamiliar with it?
Answer aloud and timed: Describe a time you faced a difficult technical challenge in production; how did you arrive at the solution?

Where candidates lose points

Own your resume

Every line on your resume is fair game. Be prepared to go into extreme detail on any project or technology you have listed.

Practice verbalizing your code

During live coding, narrate your thought process. Interviewers are often more interested in how you approach a problem than the final syntax.

Prepare for behavioral questions using the STAR method

When asked about past experiences, structure your answers by describing the Situation, Task, Action, and Result to ensure clarity.

Research the stack

Even if the job description is broad, check the technologies mentioned in recent job postings for our team and brush up on those specific tools.

Questions & practice

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

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?

Why do you want to join AI Competence Center?

Medium · Behavioral and Leadership

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
A dashboard runs this for 40 owners in one page load. What changes about the design?

Explain your most significant project in detail, focusing on your specific technical contributions.

Medium · Technical Fundamentals and Domain Expert

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

What is the difference between APIs today versus historical implementations, and how do you approach API desig

Medium · Technical Fundamentals and Domain Expert

What is the difference between APIs today versus historical implementations, and how do you approach API design?

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
Design the error taxonomy before the success shape; callers branch on it.

Follow-up

What happens if the caller retries after a timeout?
How does a client discover it is on an old version of this contract?

What are the common pitfalls of callback hell in Node.js, and how do you avoid them?

Medium · Technical Fundamentals and Domain Expert

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Can you explain the difference between React and Vue.js and when you would prefer one over the other?

Medium · Technical Fundamentals and Domain Expert

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

How would you go about building a new software application from scratch?

Medium · Problem-Solving and System Design

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How would you resolve a complex queueing process or data flow bottleneck?

Medium · Problem-Solving and System Design

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How does Kubernetes fit into your deployment workflow?

Medium · Problem-Solving and System Design

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How would you describe the internet to someone entirely unfamiliar with it?

Medium · Problem-Solving and System Design

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics AI Competence Center candidates report.

Day 1 — Map the AI Competence Center loop

Write out the reported sequence: Initial Screening, Technical Assessments, Managerial Assessments, Cross-Functional Meetings, Final Offer.
For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 5 reported rounds, with the weakest marked.

Day 2 — Work Data Structures & Algorithms (DSA)

Spend the session on Data Structures & Algorithms (DSA), which AI Competence Center candidates report being tested on.
Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures & Algorithms (DSA).

Day 3 — Work SQL / Relational Databases

Spend the session on SQL / Relational Databases, which AI Competence Center candidates report being tested on.
Write one worked example in SQL / Relational Databases and time yourself on it.

Deliverable: One timed worked example in SQL / Relational Databases.

Day 4 — Work AWS (Amazon Web Services)

Spend the session on AWS (Amazon Web Services), which AI Competence Center candidates report being tested on.
Write one worked example in AWS (Amazon Web Services) and time yourself on it.

Deliverable: One timed worked example in AWS (Amazon Web Services).

Day 5 — Answer out loud: Technical Fundamentals and Domain Expertise

Answer aloud, timed: Explain your most significant project in detail, focusing on your specific technical contributions.
Answer aloud, timed: What is the difference between APIs today versus historical implementations, and how do you approach API design?

Deliverable: Spoken answers to 2 reported Technical Fundamentals and Domain Expertise question(s), under time.

Day 6 — Answer out loud: Problem-Solving and System Design

Answer aloud, timed: How would you go about building a new software application from scratch?
Answer aloud, timed: How would you resolve a complex queueing process or data flow bottleneck?

Deliverable: Spoken answers to 2 reported Problem-Solving and System Design question(s), under time.

Day 7 — Answer out loud: Behavioral and Leadership

Answer aloud, timed: Why do you want to join AI Competence Center?
Answer aloud, timed: How do you manage your team or peers in conflict scenarios?

Deliverable: Spoken answers to 2 reported Behavioral and Leadership question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

How would you describe your experience with Java, Spring Boot, AWS, or SQL?

Medium · Technical Fundamentals and Domain Expert

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?

Describe a time you faced a difficult technical challenge in production; how did you arrive at the solution?

Medium · Problem-Solving and System Design

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?

How do you manage your team or peers in conflict scenarios?

Medium · Behavioral and Leadership

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?

Tell me about a time you had to mentor a junior developer or lead a technical initiative.

Medium · Behavioral and Leadership

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?

Are you comfortable working in a fast-paced, shifting environment?

Medium · Behavioral and Leadership

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?

Where do you see your career path evolving in the next five years?

Medium · Behavioral and Leadership

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?