How we decide
who you meet.
We can't put a percentage on an engineer. What we can do is show you exactly how we choose one, and what gets someone rejected. The method is the whole difference between a good hire and a fast one, so we publish all of it: the questions we ask, the stages we run, and the reasons people don't make it through.
You don't get a CV.
You meet a person.
Warm, direct and senior. The engineers we put in front of you can explain what they've built, why they built it that way, and what they'd do differently next time. The profiles below are illustrative examples of the engineers we vet and place, not bios of named staff.
Example profile
Barsha
Senior generalist, backend-leaning
I like the unglamorous parts: the data model nobody wants to touch, the endpoint that has to survive real traffic.
- Strongest at
- APIs, data modeling, cloud cost
- Daily AI tools
- Claude Code · Codex · Cursor
Example profile
Binod
Senior generalist, platform-leaning
If it can't be deployed, rolled back and monitored, it isn't finished. That's the whole philosophy.
- Strongest at
- AWS, CI/CD, reliability
- Daily AI tools
- Copilot · Cursor · Claude Code
Example profile
Nabin
Senior generalist, data-leaning
I've watched enough dashboards lie to want the ones that tell the truth, even when it's uncomfortable.
- Strongest at
- Data pipelines, RAG, evals
- Daily AI tools
- Claude · Codex · Aider
Example profile
Jyoti
Senior generalist, product-leaning
I ask what the user is actually trying to do before writing the first line. It usually saves a sprint.
- Strongest at
- Web apps, v2 scope, AI features
- Daily AI tools
- Cursor · Claude · Copilot
Example profile
Hemant
Senior generalist, frontend-leaning
I care about the last 10%: loading states, empty states, the things users actually feel.
- Strongest at
- React, design systems, performance
- Daily AI tools
- Cursor · Windsurf · Claude
Example profile
Sandesh
Senior generalist, mobile-leaning
A phone is a bad computer and a great opportunity. I build for what it actually is.
- Strongest at
- React Native, TypeScript, offline-first
- Daily AI tools
- Copilot · Cursor · Claude
Example profile
Prakash
Senior generalist, ops-leaning
The best incident is the one that never pages anyone. I automate the boring parts so nobody has to be brave at 3am.
- Strongest at
- Observability, Terraform, on-call
- Daily AI tools
- Claude Code · Aider · Codex
Example profile
Umesh
Senior generalist, quality-leaning
I break things on purpose before your users do it by accident. A suite that can't fail is just decoration.
- Strongest at
- Test automation, CI, release safety
- Daily AI tools
- Copilot · Claude Code · Cursor
Example profile
Rohan
Senior generalist, security-leaning
Security is mostly habits: small permissions, short-lived credentials, and code that does less than it could.
- Strongest at
- Threat modelling, IAM, secure defaults
- Daily AI tools
- Claude · Cursor · Codex
AI makes them faster.
Experience makes them useful.
We do not sell AI. We place people who use it well. Every engineer uses AI coding agents daily and can add AI features to your product when the roadmap needs them. The tools are the easy part to acquire. Knowing when the tool is wrong is the part you are hiring.
The engineer owns
- Judgment: what to build, what to skip, and what to question before it costs a sprint
- Code review, and the final word on what merges
- Talking with your team, in your tools and in your standup
- Following the work into production, and owning what happens next
AI speeds up
- Boilerplate, scaffolding and repetitive refactors, done in a fraction of the time
- First drafts of tests and documentation, before the engineer improves them
- Exploring unfamiliar code and libraries without burning a day on it
- AI features for your product: model APIs, RAG, evals, guardrails
Every AI-drafted change is read, tested and owned by the engineer who merges it. Nobody ships output they can't defend in review. That is the difference between a faster team and a faster mess.
What the process does
Every layer is a reason to stop. Most people we talk to never reach the bottom, which is the point.
Five questions.
Every engineer, every time.
None of them are about years of experience. All of them are about what happens when the work gets hard.
Can they build it?
A real problem, close to the work you'd give them. We read the code and the reasoning behind it, not the CV. The question is never whether it runs. It's whether we'd let it into a production system with our name on it.
Can they think past the ticket?
We ask what they'd do when the requirement is wrong. Good engineers push back, with a reason. Great ones can also tell you which parts of the requirement they'd keep, because the people who wrote it knew something too.
Can they work with people?
They'll be in your standup. We interview for how they explain themselves, how they disagree, and whether they ask for help before it's late. Communication problems are the most expensive problems a remote engineer can have, so we test for them on purpose.
Do they own the outcome?
We look for people who follow their work into production and care what happens after the merge. The ones who watch the dashboards, read the incident thread, and show up when their code misbehaves at 2am.
Do they use AI with judgment?
Everyone uses the tools now. What matters is whether they can tell when the tool is wrong. We ask them to critique their own AI-generated code, and to catch the confident answer that happens to be false.
The five stages
Each one has a way of ending. We've written those down too. They say more about the standard than the stages do.
- 01
We go looking
We start from your need, then go and find people who match it. Nobody is waiting on a bench. The search is narrow on purpose: we'd rather talk to five people who fit the work than fifty who roughly might.
Ends here ifNothing in their background touches the work you need done.
- 02
First conversation
A call about what they've built, what they chose, and what they'd do differently. We're listening for reasoning, not rehearsed stories. The difference between the two shows up fast when you ask a follow-up question.
Ends here ifThey can't explain their own decisions.
- 03
Technical deep-dive
A real problem, their code, our questions. We go where the work would actually go: the ugly edge cases, the failure modes, the trade-offs behind the design. Sometimes we deliberately break their solution and watch how they repair it.
Ends here ifThe code works but the reasoning doesn't hold up.
- 04
How they work with people
We push back on purpose. How someone handles disagreement tells you how the first sprint will go. We want engineers who argue the point without making it personal, and who can lose an argument gracefully when the evidence goes against them.
Ends here ifThey need a manager to translate for them.
- 05
Founder review
One question, asked by the people whose name is on the company: would we put this engineer on our own team? It's deliberately subjective. Everything before this stage is method. This is the part where we ask ourselves whether we'd sit next to this person for the next two years.
Ends here ifAny hesitation.
- →
You meet them.
One engineer, with notes on what we saw and what we weren't sure about.
What ends it
more often than anything else.
Nobody fails for being nervous. These are the things we can't get past.
- Answers that get vaguer the deeper you go.
- Work they can describe but can't take apart.
- Someone who has never been pushed back on, and doesn't take it well.
- Using AI to answer questions about their own code.
- Good on the call, no follow-through afterwards.
Then you decide.
We don't send a shortlist and hope. You get the engineer we'd pick, with what we saw and what we weren't sure about.
Interview them the way you'd interview anyone joining your team. If you're not convinced, that's useful. Tell us why and we'll go back.
And if it isn't working in the first few weeks, we'd rather be the ones to say so.
We don't disappear
after the hire.
The placement is the beginning of the relationship, not the end of it. We stay close enough to both sides to catch problems while they're still small, and honest enough to raise them before you have to. Good engagements should get better over time. That only happens if someone keeps asking.
Join
The engineer starts inside your team. Your tools, your standup, your standards. Onboarding is short by design, because we picked someone who has done this kind of work before.
Check in
We talk to both sides, not just to you, and not just once. Early conversations surface the small frictions that never make it into a status report.
Feedback
Is the contribution landing? Is communication working? Is the workload right? We ask specific questions instead of waiting for something to go visibly wrong.
Improve
We fix what's off, early, while it's still small. Sometimes that's coaching, sometimes it's a hard conversation, and occasionally it's a change of engineer. Whatever it is, you hear it from us first.
If something isn't working, we want to know early.
Where our engineers have worked
Not a list of logos. These are the kinds of systems the engineers we work with have actually built and run. If your problem rhymes with one of these, we can find you someone who has already lived with it.
Streaming & Media
Live streaming · OTT · Video · Media processing · Real-time systems. Work where the interesting problems are latency, throughput, and what happens when everyone presses play at once.
SaaS & Product
SaaS · Web · Mobile · APIs · Consumer products. The default shape of modern software: build, ship, measure, iterate, and keep it running while it grows.
Marketplaces & Commerce
Marketplaces · Auctions · Transactions · E-commerce. Systems where money moves, so correctness, idempotency and trust are features, not afterthoughts.
Enterprise & Government
Enterprise software · Government systems · Workflow platforms. Long-lived systems with real stakeholders, heavy integrations, and rules that have to be right the first time.
Cloud & Modern Engineering
AWS · Distributed systems · Cloud infrastructure · DevOps. The plumbing underneath everything else: boring on purpose, observable, and cheap to run.
A senior stack
for production systems.
Our engineers work in a modern, cloud-native stack, embedded directly in your codebase and shipping production systems with your team.
- Core
- TypeScriptNode.jsReactNext.js
- Cloud & Infrastructure
- AWSLambdaAPI GatewayDynamoDBSQSEventBridge
- Infrastructure as Code
- SSTAWS CDKTerraformPulumi
- DevOps
- GitHub ActionsDockerECSKubernetes
- Backend
- Node.jsPythonLaravelRuby on Rails
- AI
- OpenAIAnthropicAI/ML integrations
- Architecture
- ServerlessEvent-drivenCloud-nativeScalable SaaSHigh-traffic platforms
We combine modern engineering practices with AI-native development to build, modernize and scale production software.
Have the work.
Need the engineer?
Tell us what your team needs. We'll help you find the right engineer, and you'll interview them before anything is decided. If we don't have the right person, we'd rather tell you that than sell you the wrong one.
Talk to a founder, not a salesperson. A direct reply, within one business day.
We're ready for the work.