As a Software Engineer (specifically within the AI domain) at Trellissoft, you are not merely writing code; you are architecting intelligent systems that redefine how enterprises interact with complex data. You will work on flagship platforms like PulseAI360, DocuVera360, and RelyBill, helping to transition advanced research into production-grade, scalable solutions. Your work directly impacts industries ranging from BFSI and healthcare to telecom, ensuring that AI-driven conversational intelligence and document automation are both reliable and performant. This role is critical because it demands a balance between the "hacker" mindset—rapidly prototyping new research—and the discipline required for production-grade deployment. You will be at the forefront of Agentic Workflows, RAG architectures, and Small Language Models (SLMs), solving real-world challenges like hallucination reduction and low-latency model serving. It is a high-stakes, high-impact position designed for engineers who want to see their code provide tangible ROI for global users.
Initial Screening
reportedGauge your interest and background through an initial contact.
What to demonstrate
- Gauge your interest and background through an initial contact
- Depth in RAG (Retrieval-Augmented Generation)
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 Discussions
reportedEngage in rigorous technical discussions, often involving hands-on coding sessions.
What to demonstrate
- Engage in rigorous technical discussions, often involving hands-on coding sessions
- Depth in RAG (Retrieval-Augmented Generation)
How to prepare
- Answer aloud and timed: Walk us through your process for implementing Agentic Workflows—how do you ensure the model effectively uses external tools?
- Answer aloud and timed: What are the primary challenges when working with Vision Transformers (ViT) for document layout analysis?
Project Deep-Dive
reportedDiscuss your past projects, focusing on real-world applications and challenges.
What to demonstrate
- Discuss your past projects
- Focusing on real-world applications and challenges
How to prepare
- Answer aloud and timed: Can you describe a time you had to troubleshoot a model producing hallucinations?
- Answer aloud and timed: How do you structure a data pipeline for Vector Databases like Chroma or Pinecone to ensure efficient retrieval?
PracHub editorial advice for the preparation topics above.
Show your work
When discussing past projects, focus on the "how" and "why." Explain the specific challenges you faced and the trade-offs you made in your architecture.
Be ready for technical depth
Do not just list tools on your resume. Be prepared to explain how they work under the hood, especially regarding RAG and Transformer architectures.
Align with business value
Trellissoft is focused on ROI. Always frame your technical solutions in the context of how they solve real-world problems for their enterprise clients.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Find overlapping job attempts and peak concurrency from lease records
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?
Track a rolling failure rate per destination for circuit decisions
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?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Write the update path that detects a concurrent edit
resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.
Approach
- One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
- Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
- State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
- Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.
Follow-up
- A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
- Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?
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?
Explain the difference between RAG architectures and fine-tuning; when would you choose one over the other?
Explain the difference between RAG architectures and fine-tuning; when would you choose one over the other?
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 optimize Small Language Models (SLMs) for low-latency deployment in a production environment?
How do you optimize Small Language Models (SLMs) for low-latency deployment in a production environment?
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?
Walk us through your process for implementing Agentic Workflows—how do you ensure the model effectively uses e
Walk us through your process for implementing Agentic Workflows—how do you ensure the model effectively uses external tools?
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 primary challenges when working with Vision Transformers (ViT) for document layout analysis?
What are the primary challenges when working with Vision Transformers (ViT) for document layout analysis?
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 structure a data pipeline for Vector Databases like Chroma or Pinecone to ensure efficient retrieva
How do you structure a data pipeline for Vector Databases like Chroma or Pinecone to ensure efficient retrieval?
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 system for real-time speech-to-text diarization that maintains high accuracy under varied audio condi
Design a system for real-time speech-to-text diarization that maintains high accuracy under varied audio conditions.
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 implement evaluation frameworks (e.g., RAGAS) to quantify the performance and bias of an AI system?
How do you implement evaluation frameworks (e.g., RAGAS) to quantify the performance and bias of an AI 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?
Given a specific enterprise requirement, how would you decide between using a closed-source model versus an op
Given a specific enterprise requirement, how would you decide between using a closed-source model versus an open-source model like Llama or Mistral?
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?
One customer endpoint stalls deliveries to every other destination
The egress service delivers about 1.5k webhooks/second across 40,000 destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. Throughput falls to 300/second, queue depth climbs, and p99 delivery latency for unaffected destinations goes from 200 ms to minutes, while the error rate barely moves. One tenant holds 900 destination rows whose URLs share a hostname that now answers in 9.5 seconds. Explain the mechanism with the arithmetic, then give the containment in the order you would apply it.
Approach
- Look at saturation before errors. A flat error rate with collapsing throughput says nothing is failing, things are waiting, so the first signal to pull is in-flight request count or pool wait time rather than the error counter. This is the distinction that decides the whole investigation.
- Group in-flight work by resolved host, not by destination id. The cap is keyed per destination row, so 900 rows sharing one hostname buy 3,600 concurrent slots against a single host, each held for 9.5 seconds. The bulkhead was never a bulkhead for that host, and grouping by the wrong dimension is why the dashboard looked healthy.
- Do the arithmetic in both directions. Required concurrency is arrival rate times latency, so 1.5k/second at 200 ms needs about 300 in flight, which is entirely consumed by 3,600 slow slots; conversely whatever concurrency is left sustains rate equals concurrency divided by 9.5 seconds, which is the 300/second you are seeing. Matching both numbers is what promotes this from a plausible story to the mechanism.
- Explain why the circuit breaker never helped. It opens on consecutive failures, and a 9.5-second response inside a 10-second timeout is a success. Slow is not failing, so an error-rate breaker cannot see this; you need a slow-call ratio, a deadline propagated from the caller's remaining budget, or a concurrency limiter.
Follow-up
- The host recovers to 80 ms. How long does the queue take to drain, and what does the drain do to the recovered host?
- Where should the 10-second timeout number actually come from?
Built from the rounds and topics Trellissoft candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Trellissoft loop
- Write out the reported sequence: Initial Screening, Technical Discussions, Project Deep-Dive.
- 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 RAG (Retrieval-Augmented Generation)
- Spend the session on RAG (Retrieval-Augmented Generation), which Trellissoft candidates report being tested on.
- Write one worked example in RAG (Retrieval-Augmented Generation) and time yourself on it.
Deliverable: One timed worked example in RAG (Retrieval-Augmented Generation).
03Work Agentic System Design
- Spend the session on Agentic System Design, which Trellissoft candidates report being tested on.
- Write one worked example in Agentic System Design and time yourself on it.
Deliverable: One timed worked example in Agentic System Design.
04Work Python
- Spend the session on Python, which Trellissoft candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
05Answer out loud: Technical Proficiency & AI Fundamentals
- Answer aloud, timed: Explain the difference between RAG architectures and fine-tuning; when would you choose one over the other?
- Answer aloud, timed: How do you optimize Small Language Models (SLMs) for low-latency deployment in a production environment?
Deliverable: Spoken answers to 2 reported Technical Proficiency & AI Fundamentals question(s), under time.
06Answer out loud: Problem-Solving & System Design
- Answer aloud, timed: How do you structure a data pipeline for Vector Databases like Chroma or Pinecone to ensure efficient retrieval?
- Answer aloud, timed: Design a system for real-time speech-to-text diarization that maintains high accuracy under varied audio conditions.
Deliverable: Spoken answers to 2 reported Problem-Solving & System Design question(s), under time.
07Answer out loud: Behavioral & Professional Alignment
- Answer aloud, timed: Describe a time you had to pivot your technical approach based on a new research paper or GitHub release.
- Answer aloud, timed: How do you balance the need for rapid prototyping with the requirement for stable, production-ready code?
Deliverable: Spoken answers to 2 reported Behavioral & Professional Alignment 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.
Can you describe a time you had to troubleshoot a model producing hallucinations?
Can you describe a time you had to troubleshoot a model producing hallucinations?
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 had to pivot your technical approach based on a new research paper or GitHub release.
Describe a time you had to pivot your technical approach based on a new research paper or GitHub release.
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 balance the need for rapid prototyping with the requirement for stable, production-ready code?
How do you balance the need for rapid prototyping with the requirement for stable, production-ready code?
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?
Talk about a project where you had to explain complex AI outcomes to non-technical stakeholders.
Talk about a project where you had to explain complex AI outcomes to non-technical stakeholders.
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?
- 01
Can you describe a time you had to troubleshoot a model producing hallucinations?
- 02
Describe a time you had to pivot your technical approach based on a new research paper or GitHub release.
- 03
How do you balance the need for rapid prototyping with the requirement for stable, production-ready code?
- 04
Talk about a project where you had to explain complex AI outcomes to non-technical stakeholders.
How difficult are the interviews?
Candidates generally describe the process as rigorous but fair. The interviewers are knowledgeable and focus on practical application, so expect to be challenged on your technical choices.
Trellissoft Software Engineer candidate reports ↗What differentiates successful candidates?
The most successful candidates are those who demonstrate a "hacker" mindset—a genuine passion for keeping up with the latest AI research and the ability to quickly translate that knowledge into functional, production-ready code.
Trellissoft Software Engineer candidate reports ↗Is this a remote role?
Trellissoft operates globally; check the specific job listing for location requirements, as some roles may be tied to hubs like Panaji or Bengaluru.
Trellissoft Software Engineer candidate reports ↗What is the typical timeline?
While processes vary, you should expect a multi-round process involving technical and management discussions. Clear communication with your recruiter regarding your timeline is recommended.
Trellissoft Software Engineer candidate reports ↗What topics does Trellissoft test in interviews?
Trellissoft interviews most often cover RAG (Retrieval-Augmented Generation), Agentic System Design, Python, LLM Orchestration, and Fine-tuning LLMs. The exact emphasis depends on the specific role you apply for.
Trellissoft 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.
- 01Trellissoft 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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