As a Software Engineer at Onlinesales.ai, you will be responsible for building and scaling high-performance systems that sit at the intersection of e-commerce, advertising technology, and machine learning. You are expected to contribute to the development of robust, data-driven applications that handle complex workflows, such as dynamic ad management, automated bidding strategies, and large-scale data processing. This role is critical to the company’s mission of optimizing online retail performance. You will engage with complex architectural challenges, requiring a deep understanding of distributed systems, database optimization, and efficient algorithmic problem-solving. Whether you are working on microservices for ad-tech workflows or optimizing core database queries for massive datasets, your work directly influences the efficiency and scalability of the Onlinesales.ai platform. Candidates joining the team should be prepared for a fast-paced environment where technical rigor and system-thinking are paramount. The role demands more than just coding ability; it requires a mindset focused on trade-offs, performance, and the ability to design systems that are both resilient and adaptable to changing business requirements.
Initial Screening
reportedThe process begins with an initial screening to assess candidate qualifications.
What to demonstrate
- The process begins with an initial screening to assess candidate qualifications
- 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 Rounds
reportedMultiple technical rounds test coding proficiency, system design skills, and foundational knowledge.
What to demonstrate
- Multiple technical rounds test coding proficiency, system design skills, and foundational knowledge
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Implement an LRU Cache.
- Answer aloud and timed: Solve the "Shipping Container" problem (a variation of the painter partition/split array sum problem).
Bar Raiser Round
reportedA senior leader evaluates your potential to maintain the company’s technical and cultural standards.
What to demonstrate
- A senior leader evaluates your potential to maintain the company’s technical and cultural standards
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Given an array of prime numbers, find the one that occurs more than once (O(N) time, O(1) space).
- Answer aloud and timed: Design a UPI (Unified Payments Interface) workflow system.
PracHub editorial advice for the preparation topics above.
Master the fundamentals
Do not neglect basic SQL and OOPS concepts, as these appear frequently alongside complex system design questions.
Practice pen-and-paper coding
Some interview rounds may require you to write code without the aid of an IDE or syntax highlighting.
Focus on communication
When solving a coding problem, think out loud. Interviewers want to see how you break down a complex problem into manageable steps.
Prepare for the Bar Raiser
This round is about your long-term potential. Be ready to discuss your past projects, the challenges you faced, and how you contributed to team success.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write code to count occurrences of a specific number in an array without using the equality operator.
Write code to count occurrences of a specific number in an array without using the equality operator.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Find the equilibrium point in an array with an expected time complexity of O(N).
Find the equilibrium point in an array with an expected time complexity of O(N).
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Implement an LRU Cache.
Implement an LRU Cache.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Given an array of prime numbers, find the one that occurs more than once (O(N) time, O(1) space).
Given an array of prime numbers, find the one that occurs more than once (O(N) time, O(1) space).
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
How would you structure a database schema to support high-concurrency transactions?
How would you structure a database schema to support high-concurrency transactions?
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?
How do you optimize complex SQL queries involving joins across multiple tables?
How do you optimize complex SQL queries involving joins across multiple tables?
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?
Denormalise tenant onto revisions and backfill it live
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?
Solve the "Shipping Container" problem (a variation of the painter partition/split array sum problem).
Solve the "Shipping Container" problem (a variation of the painter partition/split array sum problem).
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?
Design a UPI (Unified Payments Interface) workflow system.
Design a UPI (Unified Payments Interface) workflow system.
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?
Design an activity logger service for a trading application.
Design an activity logger service for a trading application.
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?
Design a URL shortener or a distributed cache.
Design a URL shortener or a distributed cache.
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?
Discuss microservices architecture and how to handle service-to-service communication.
Discuss microservices architecture and how to handle service-to-service communication.
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?
Explain how you would design a blog platform or a dynamic workflow engine.
Explain how you would design a blog platform or a dynamic workflow engine.
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?
Explain the difference between various indexing strategies and their impact on read/write performance.
Explain the difference between various indexing strategies and their impact on read/write performance.
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?
Edge instances grow 400 MB per hour until the nightly restart
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?
Built from the rounds and topics Onlinesales.ai candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Onlinesales.ai loop
- Write out the reported sequence: Initial Screening, Technical Rounds, Bar Raiser Round.
- 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.
02Work Data Structures & Algorithms (DSA)
- Spend the session on Data Structures & Algorithms (DSA), which Onlinesales.ai 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).
03Work Coding Interviews
- Spend the session on Coding Interviews, which Onlinesales.ai candidates report being tested on.
- Write one worked example in Coding Interviews and time yourself on it.
Deliverable: One timed worked example in Coding Interviews.
04Work System Design
- Spend the session on System Design, which Onlinesales.ai candidates report being tested on.
- Write one worked example in System Design and time yourself on it.
Deliverable: One timed worked example in System Design.
05Answer out loud: Data Structures and Algorithms
- Answer aloud, timed: Write code to count occurrences of a specific number in an array without using the equality operator.
- Answer aloud, timed: Find the equilibrium point in an array with an expected time complexity of O(N).
Deliverable: Spoken answers to 2 reported Data Structures and Algorithms question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: Design a UPI (Unified Payments Interface) workflow system.
- Answer aloud, timed: Design an activity logger service for a trading application.
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Database and SQL
- Answer aloud, timed: How do you optimize complex SQL queries involving joins across multiple tables?
- Answer aloud, timed: Explain the difference between various indexing strategies and their impact on read/write performance.
Deliverable: Spoken answers to 2 reported Database and SQL question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Estimate work you have never done and defend the range
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
Approach
- Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
- Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
- Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
- Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.
Follow-up
- How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
- Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?
Ship under a deadline and bound the debt you chose
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
Approach
- Name the deferred failure precisely instead of calling it slow. OFFSET n makes the database produce and discard n rows, so cost grows with page depth; without an index matching the sort, every matching row is read and sorted before the limit applies; and rows inserted between two page fetches shift across the boundary so items are skipped or repeated with nothing in the response to signal it.
- Bound the blast radius with something mechanical rather than a promise: cap maximum page depth, cap page size, restrict the endpoint to one internal caller, or keep it behind a flag. State which failure each cap removes and which it leaves standing.
- Attach a number to the trigger and wire it to an alarm: the first tenant crossing N resources, or the endpoint's p99 crossing its share of the 400 ms budget, so the debt announces itself instead of waiting to be remembered.
- Write it where the next engineer looks, which is the code and the ticket, not a chat message: what was deferred, why, the cap, and the trigger.
Follow-up
- At what page depth does the offset version breach your latency budget, given your page size and row counts?
- What breaks first when you switch to keyset pagination later, and what does a client holding an old page token see?
Narrate an outage you owned from page to postmortem
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
Approach
- Open on the signal rather than the cause: which metric at which percentile moved, on which service, at what time, so the listener follows the same evidence you had rather than a conclusion you already reached.
- Separate mitigation from diagnosis out loud. State what you did to stop the bleeding (flag off, shed traffic, drain a lease, roll back a deploy) and say plainly that you did it before the mechanism was known, because those are two jobs with different deadlines.
- Establish blast radius in countable terms: how many tenants, how many writes, and crucially whether the effect was loss or only delay. An append-only revision table or a pending outbox row means the change survived and the projection was merely behind, which is a repair rather than a data-loss incident.
- Prove the mechanism instead of asserting it. Name the trace span that grew, the plan that flipped to a sequential scan, the lease that expired, plus one alternative you ruled out and the signal that stayed flat while you ruled it out.
Follow-up
- What would you do differently in the first five minutes, given the same dashboard and no more information?
- Which follow-up action did you deliberately not take, and why was dropping it the right call?
- 01
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
- 02
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
- 03
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
How difficult are the interviews?
Candidates generally report the interviews as moderate to difficult. The focus on deep technical knowledge and system design means that superficial preparation is often insufficient.
Onlinesales.ai Software Engineer candidate reports ↗What is the best way to prepare for the system design rounds?
Focus on real-world scenarios, such as designing a caching system or a workflow engine. Practice articulating the trade-offs between different database schemas and service architectures.
Onlinesales.ai Software Engineer candidate reports ↗How long does the process take?
The process can involve multiple rounds and may take several weeks. It is important to stay engaged with your recruiter throughout the timeline.
Onlinesales.ai Software Engineer candidate reports ↗What differentiates successful candidates?
Successful candidates are those who can explain the "why" behind their technical decisions and who remain calm and collaborative when faced with complex, ambiguous design problems.
Onlinesales.ai Software Engineer candidate reports ↗How many interview rounds does Onlinesales.ai have for Software Engineer, and how does the process loop run?
Onlinesales.ai starts with an initial screening, then runs multiple technical rounds. The topics span coding proficiency, system design skills, and foundational knowledge, and the process concludes with a Bar Raiser round where a senior leader evaluates technical and cultural standards.
Onlinesales.ai Software Engineer candidate reports ↗What topics does Onlinesales.ai test for Software Engineer interviews?
Expect Data Structures and Algorithms, coding interviews, and system design. You should also be ready for UPI system design, SQL, and database design, with specific emphasis on things like caching systems such as LRU cache and general problem solving.
Onlinesales.ai Software Engineer candidate reports ↗Does Onlinesales.ai ask Software Engineer candidates to implement an LRU Cache?
Yes. An example public sample question for this role is: Implement LRU Cache. Since LRU cache is listed among the top tested topics, you should be prepared to implement it and discuss complexity and edge cases.
Onlinesales.ai Software Engineer candidate reports ↗What pay should I expect for Onlinesales.ai Software Engineer interviews?
The provided materials do not include specific compensation figures for Onlinesales.ai Software Engineer. Because no base or total salary numbers are stated in the supplied text, you should not rely on pay estimates from these notes.
Onlinesales.ai Software Engineer candidate reports ↗What should I prioritize when preparing for Onlinesales.ai Software Engineer interviews?
Prioritize showing strong DSA fundamentals with clear discussion of time and space complexity during coding rounds. For system design, focus on trade-offs and system thinking, including error handling, fault tolerance, and how to choose between alternatives rather than giving a single design.
Onlinesales.ai Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Onlinesales.ai Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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