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Sajix Software Solution Private Software Engineer Interview Guide

Sajix Software Solution Private Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

At Sajix Software Solution Private, a Software Engineer plays a pivotal role in designing, building, and maintaining high-performance healthcare IT systems. The company specializes in creating comprehensive healthcare portals that cater to both Outpatient (OP) and Inpatient (IP) environments. As a member of the engineering team, you will build the core products and features that allow medical providers to manage patient care, streamline clinical workflows, and handle complex data securely. This position is highly impactful because the software you write directly affects clinical operations and patient outcomes. You will work on a modern technology stack where Python is central to backend development, alongside robust web application frameworks and automation tools.

This guide is scoped to a Software Engineer candidate at Sajix Software Solution Private.

Focus areas

Programming in Python
Test Automation (Selenium)
Problem Solving (Coding/Exercises)

The Sajix Software Solution Private interview process

Sajix Software Solution Private candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Resume Screening

Initial review of your resume to assess qualifications and fit for the role.

What to demonstrate

Initial review of your resume to assess qualifications and fit for the role
Depth in Programming in Python

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.

HR Conversation

Brief phone or video conversation with HR to discuss your background and career goals.

What to demonstrate

Brief phone or video conversation with HR to discuss your background and career goals
Depth in Programming in Python

How to prepare

Answer aloud and timed: How do you secure RESTful APIs to prevent unauthorized access to sensitive patient data?
Answer aloud and timed: Write a Python script to parse a complex JSON response and extract specific nested elements.

Technical Interview Rounds

Series of practical interviews focusing on real-world scenarios relevant to the job.

What to demonstrate

Series of practical interviews focusing on real-world scenarios relevant to the job
Depth in Programming in Python

How to prepare

Answer aloud and timed: How do you manage database migrations and state changes in a production environment?
Answer aloud and timed: What are the key architectural differences to consider when designing features for an Inpatient (IP) portal versus an Outpatient (OP) portal?

Where candidates lose points

Focus on Clean Code

When writing code or automation scripts during your interviews, prioritize readability, maintainability, and proper error handling. Explain your architectural choices clearly.

Always explain your thought process out loud

Interviewers at Sajix are just as interested in how you arrive at a solution as they are in the final code itself.

Brush Up on Automation Tools

Be ready to discuss the technical details of writing Selenium scripts, such as handling dynamic elements, managing wait times, and structuring test suites in Test Rail.

Understand the Domain

Take some time to familiarize yourself with basic healthcare workflows, particularly the differences between outpatient visits and inpatient hospitalizations. This contextual knowledge will help you design better software solutions during system design questions.

Questions & practice

What are the differences between synchronous and asynchronous programming in Python, and when would you use ea

Medium · Python & Web Application Development

What are the differences between synchronous and asynchronous programming in Python, and when would you use each?

Approach

Say what the runtime actually does before reasoning about the code.
Name what is shared across threads and what owns each piece of state.
Identify the window where an invariant is briefly untrue.
Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

What happens if two callers reach this at the same time?
Where could this allocate more than you expect?

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?

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?

Explain how you would optimize a slow-performing database query in a Python web application.

Medium · Python & Web Application Development

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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

How do you secure RESTful APIs to prevent unauthorized access to sensitive patient data?

Medium · Python & Web Application Development

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?

Write a Python script to parse a complex JSON response and extract specific nested elements.

Medium · Python & Web Application Development

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 do you manage database migrations and state changes in a production environment?

Medium · Python & Web Application Development

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 are the key architectural differences to consider when designing features for an Inpatient (IP) portal ve

Medium · Healthcare Domain & Portal Workflows

What are the key architectural differences to consider when designing features for an Inpatient (IP) portal versus an Outpatient (OP) portal?

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 design a patient check-in workflow that minimizes wait times and syncs data across clinical modu

Medium · Healthcare Domain & Portal Workflows

How would you design a patient check-in workflow that minimizes wait times and syncs data across clinical modules in real time?

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 do you ensure data integrity when transferring patient records between different healthcare systems?

Medium · Healthcare Domain & Portal Workflows

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 do you write a robust Selenium script to automate the testing of a multi-step user registration form?

Medium · Test Automation & Quality Assurance

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 your strategy for managing test suites and tracking test coverage using tools like Test Rail?

Medium · Test Automation & Quality Assurance

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?

Describe your process for identifying, documenting, and prioritizing critical software bugs in Bugzilla.

Medium · Test Automation & Quality Assurance

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 are the benefits of integrating automated regression tests into a CI/CD pipeline?

Medium · Test Automation & Quality Assurance

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?

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 Sajix Software Solution Private candidates report.

Day 1 — Map the Sajix Software Solution Private loop

Write out the reported sequence: Resume Screening, HR Conversation, Technical Interview Rounds.
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 3 reported rounds, with the weakest marked.

Day 2 — Work Programming in Python

Spend the session on Programming in Python, which Sajix Software Solution Private candidates report being tested on.
Write one worked example in Programming in Python and time yourself on it.

Deliverable: One timed worked example in Programming in Python.

Day 3 — Work Test Automation (Selenium)

Spend the session on Test Automation (Selenium), which Sajix Software Solution Private candidates report being tested on.
Write one worked example in Test Automation (Selenium) and time yourself on it.

Deliverable: One timed worked example in Test Automation (Selenium).

Day 4 — Work Problem Solving (Coding/Exercises)

Spend the session on Problem Solving (Coding/Exercises), which Sajix Software Solution Private candidates report being tested on.
Write one worked example in Problem Solving (Coding/Exercises) and time yourself on it.

Deliverable: One timed worked example in Problem Solving (Coding/Exercises).

Day 5 — Answer out loud: Python & Web Application Development

Answer aloud, timed: Explain how you would optimize a slow-performing database query in a Python web application.
Answer aloud, timed: What are the differences between synchronous and asynchronous programming in Python, and when would you use each?

Deliverable: Spoken answers to 2 reported Python & Web Application Development question(s), under time.

Day 6 — Answer out loud: Healthcare Domain & Portal Workflows

Answer aloud, timed: What are the key architectural differences to consider when designing features for an Inpatient (IP) portal versus an Outpatient (OP) portal?
Answer aloud, timed: How would you design a patient check-in workflow that minimizes wait times and syncs data across clinical modules in real time?

Deliverable: Spoken answers to 2 reported Healthcare Domain & Portal Workflows question(s), under time.

Day 7 — Answer out loud: Test Automation & Quality Assurance

Answer aloud, timed: How do you write a robust Selenium script to automate the testing of a multi-step user registration form?
Answer aloud, timed: What is your strategy for managing test suites and tracking test coverage using tools like Test Rail?

Deliverable: Spoken answers to 2 reported Test Automation & Quality Assurance question(s), under time.

Behavioural preparation

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

Describe how you would handle complex scheduling conflicts when booking appointments for multiple doctors acro

Medium · Healthcare Domain & Portal Workflows

Describe how you would handle complex scheduling conflicts when booking appointments for multiple doctors across different departments.

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 handle dynamic elements and asynchronous loading when writing web automation scripts?

Medium · Test Automation & Quality Assurance

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?

Argue against a design, lose, and commit anyway

Medium · disagreement · service boundaries · decision records

Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.

Approach

State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.

Follow-up

What threshold on that alert would have proved you right, and did anyone ever look at it?
If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?