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.
Compare engineering roles and engagement models, then review how Bigcircle selects engineers and brings them into your team.
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
All-rounders who ship a whole AI feature end to end, or anchor a pod of the specialists below.
LLM_ENGINEERING
Model specialists who fine-tune, quantise, and serve the model itself.
AI_AGENTS
Builders of agents that plan and take real actions in your systems, safely.
RAG_RETRIEVAL
Retrieval specialists who ground every answer in your data.
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
The infra people who keep AI fast, efficient, and observable in production.
VOICE_AI
Realtime voice agents that answer in under a second.
DATA_ENGINEERING
The pipelines that turn raw data into retrieval-ready, training-ready fuel.
PROMPT_EVAL
The people who turn AI quality into a number you can ship against.
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.
Every engineer we present has cleared a five-stage vetting gauntlet: an applied generative-AI build, a live system-design review, a communication screen, and a frontier-knowledge check, on top of resume screening. Historically fewer than three in a hundred applicants make it through. You only ever meet that group.
Because we match from engineers who are already vetted, most clients see a shortlist within two to five business days and have someone embedded inside two weeks. There is no months-long sourcing cycle.
Many of our engineers hold Anthropic certification and build on Claude daily, alongside OpenAI and open models. It is a signal that they are current with frontier tooling and best practice, not a one-off course. We will tell you which certifications each engineer holds.
Yes. Engineers embed full-time in your timezone window, in your tools and standups. They are dedicated to your team, not split across many clients.
You start with an interview and an optional paid trial sprint on real work, so you confirm fit before committing. If something is not working after that, we replace the engineer quickly, at no extra cost.
You do. Engineers work in your repositories under your IP and confidentiality terms. We handle employment, payroll, and compliance on the back end.
Yes. Beyond individual hires, we run managed AI pods and fixed-scope outcome engagements where we own delivery end to end. Same engineers, more of the accountability on us.
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.