Engineers who have built and shipped production AI, many of them Anthropic-certified, embedded in your team in days. Get proven talent and skip the part where AI stalls before it ever reaches customers.
Roles are separated by the break they can fix: a silent parser, a missing passage, a crew with no ceiling, a voice line that talks over the caller.
Compare that break, then read how Bigcircle vets the engineer: a system they already built, then something broken for them to fix. They embed in your repositories and you own the code.
Bigcircle only presents engineers who have cleared a five-stage vetting gauntlet, and the top 3% of applicants remain. Vetting starts from a system they already built, then breaks something and watches them fix it, asks how they would know it is working, asks them to make it fast without running up the bill, and asks what they do first when it is late and the AI got it wrong. Most clients see a shortlist within two to five business days, then interview or run an optional paid trial before an engineer embeds full-time in your repositories. You own the code, and if the match is not working Bigcircle replaces the engineer at no extra cost.
Start with one engineer and scale to a full pod, or hand us a scoped outcome to own end to end. The only thing that changes is how much of the delivery we run.
If the question is us, a full-time hire, or an agency, use the staffing guide. If you already want us and you are choosing embed, a CTO-led pod, or a fixed-scope build, use the engagement guide.
A vetted generative-AI engineer embeds in your team and your codebase, full-time, in your timezone window. You set priorities and own the code. We handle employment, payroll, and compliance.
Best when you have the direction and the team, but need real AI depth on the build.
A managed squad of AI engineers, a tech lead, and QA. We bring the delivery rhythm and share accountability for each sprint. You bring the product context and the roadmap.
Best when you have a roadmap but no AI team to run it.
You bring the problem, we deliver the working AI system. Discovery, build, evals, and deployment under one point of accountability against a fixed scope and price.
Best when you want the result, not the hiring and management overhead.
Not a vendor you brief and wait on. The mechanics of an embedded engineer, day to day, so you know what you are agreeing to.
They join your repositories, use your tools, attend your standups, and match your sprint cadence. No handholding, no parallel process to maintain, no months of onboarding overhead.
Dedicated to your team rather than split across three clients. A daily working overlap with US, EU, or APAC hours, so review and unblocking happen the same day rather than the next one.
Work happens in your repositories under your IP and confidentiality terms. We are not a black box you brief and wait on; the engineer takes direction from your leads like any other member of the team.
Scale up when the roadmap does and keep the same people, the same context, and the same accountability. Growing the team does not mean re-explaining the system to strangers.
The usual objection to hiring this way is what happens when it goes wrong. Three answers, before you ask.
An interview, then an optional paid trial sprint on real work from your backlog. You judge the engineer on your codebase and your problems, not on a CV and a call.
Quickly, and at no extra cost. No renegotiation, no restarting the search, no arguing about whose fault it was. The risk of a bad match sits with us, not with your roadmap.
Employment, payroll, and compliance sit with us on the back end. No entity to open, no contractor classification to defend, no employment law to learn.
Hiring for this is the slowest part of your roadmap.
Enterprise insight
80%of enterprise leaders already bring in external partners on AI.
Roughly 80% of enterprise leaders already engage outside partners on their AI initiatives, and only 7% say they never plan to. The teams pulling ahead are not the ones hiring slowest. They are the ones who got proven AI engineers building first.
From the model itself to the infrastructure under it to the interface on top. Hire one all-rounder, a deep specialist, or a full pod across every role below.
GENERATIVE_AI
Engineers who ship a feature with a source behind every RAG answer. The eval can fail the release.
LLM_ENGINEERING
Engineers who fine-tune against your rules and publish the comparison with the base model. Serving changes only when that comparison says the quality held.
AI_AGENTS
Engineers who stop a bad tool call before it runs. Every action is traced so it can be replayed.
RAG_RETRIEVAL
Engineers who measure retrieval apart from the answer. Chunking is part of the job, because a missing passage cannot be fixed in the prompt.
LANGCHAIN
Engineers who know which LangChain abstractions to keep and which to drop.
LLAMAINDEX
Retrieval engineers who work the ingestion path, not only the vector store.
CREWAI
Multi-agent engineers who bound run cost and delegation before anything runs unattended.
MLOPS_INFRA
Engineers who roll a model or prompt forward with a rollback. They have carried a pager for production AI.
VOICE_AI
Realtime voice agents that answer in under a second.
DATA_ENGINEERING
Engineers who keep a pipeline traceable from the source to retrieval and training. A retrieval bug is often a data bug, so the path is the job.
PROMPT_EVAL
Engineers who turn a quality feeling into a suite that can block a merge. Prompts are versioned, and a red-team case sits next to the happy path.
Calling a model is the easy part. This is the engineering that decides whether the feature survives real traffic, real cost, and real customers.
Every stage asks the same question: can they keep AI running once real customers are using it? Getting something started is the easy part, and it is not what we screen for.
400 applicants
3%of applicants reach
your shortlist
What removes them
One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?
A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.
Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.
We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.
What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?
We match, scope, and deploy fast. No long recruiting cycle, no resume pile, no onboarding drag. Just the right engineer in your repo, quickly.
A 30-minute call. The AI problem, your stack, the seniority you need, and how your team works. No long intake forms.
We match from engineers who have already cleared vetting, so you see a curated shortlist with real work samples, not a stack of resumes to sift.
Run your own interview, or a paid trial sprint on a real task. You confirm the fit before anything is locked in.
Your engineer embeds in your tools and rituals. We handle contracts, payments, and compliance, and you can scale the team up or down as the work changes.
200+ systems in production. Not one of them a demo.
Real builds for named clients, each with something you can check: a capability shipped first, a live demo, a system still in production.
Tool-discovery shipped before the labs standardized it
A desktop AI chief-of-staff that plans a goal, acts across email, calendar, docs, and CRM, and checks its own work, with credentials that never leave the device. Our engineers shipped dynamic tool-discovery for it months before the labs made it a standard.
Solarpunk · Executive operationsAmong the first in the world to ship live-meeting detection
With Nurix, our engineers built the real-time engine behind AnyTeam: a sales copilot that detects a live meeting and transcribes it on-device, roughly second in the world to ship live-meeting detection, inside a signed, notarized desktop app with an embedded local model.
Nurix · Sales copilotsCitation-grounded where general models guess
Agentic search over dense ESG and IMF filings: it grounds every answer in a citation and fact-checks new documents against the knowledge base. Grounded document intelligence for an institutional client, on filings general chatbots get wrong.
Still have a question? Mail hello@bigcircle.ai and we’ll get back within two hours on weekdays.
You don’t need more resumes. You need proven AI engineers embedded in your workflow and ready to build from day one. Tell us what’s missing and we’ll line up a shortlist.