For AI features shipped into a real product, not a proof of concept

Hire AI Developers

Most AI features fail in the product decisions around the model, not the model itself. No fallback for a timeout, no forecast for cost, no plan for a prompt that quietly degrades. These engineers are tested on exactly those decisions, then placed to ship AI capability into a product with real users.

  • Profiles typically within two business days
  • Replacement Assurance: If an engineer is not the right fit, a replacement from our own team starts within ten business days of your request, at no extra cost
  • You interview before placement if you want to
  • At least four hours a day inside your core working hours. Two hours plus a daily written handover for the US West Coast
  • 14-Day Risk-Free Trial: For your first engineer on a time-and-materials engagement. Up to 14 calendar days or 80 logged working hours, whichever comes first. End within the trial and pay nothing for trial hours. Continue beyond it and those hours are billed at the agreed rate. Unpaid trial work remains ours if you stop
Get MatchedBook a Call
Shipped into the product, not a demoReplacement is our cost, not yoursAI augmented, human governedEvidence before estimatesYour IP from day oneOne accountable owner per engagementFounded in 2011. Part of the Vinove Group500+ full-time engineers, as of September 2026NASSCOM member
Watch · the 90-second brief

How we evaluate an AI developer

The tasks, the signals and the onboarding, walked through by the people who run them.

90 secWatch without filling in a form.
Engineers reviewing an AI feature integration together.90 secSample frame · video pending

01Published, not promised

The assessment behind every engineer we send.

The AI developer bar is published, not assumed: five competencies, the exact task behind each, and the shortcut that ends a candidacy. Most interviews ask which library someone has used. This one asks whether the feature is safe to ship.

What we listen forWhether they scope the integration safely, not just wire up an API call.

The task we set

A team wants to add an AI feature to an existing app without destabilising the rest of the codebase. Propose your approach.

What strong sounds like
Proposes a clearly bounded integration point with fallback behaviour defined before any AI call touches production traffic.
What gives us pause
Proposes wiring the API call directly into the existing request path without considering failure isolation.
Core

Python · JavaScript · LangChain/LlamaIndex

LLM

OpenAI API · Anthropic API · Prompt Engineering

Data

Vector Databases · Embeddings

Tooling

Docker · CI/CD · Observability

02Who you actually meet

The bench, priced by seniority.

Three seniority tiers, priced honestly, and the engineers who fill each one today. Real names and photos take their place as soon as we can share them, and the standard stays exactly the same.

Engineer

Features scoped tightly enough to ship inside your review bar, every time.

The right first hire when your seniors already set the standard.

$2,800–3,600per month

Senior

The hard module nobody else wants to own, taken end to end.

Ownership of a surface, without adding a manager to your headcount.

$3,900–4,900per month

Lead

The workstream's decisions, made daily, so nothing waits on a meeting.

One accountable voice for a pod or initiative that can't afford to drift.

$5,200–6,500per month

  • AI developer

    3 yrs · US East overlap

    Shipped a document Q&A feature that scaled from a ten-file demo to a full library

    • Python
    • LlamaIndex
    • Embeddings

  • Senior AI developer

    6 yrs · UK + EU hours

    Gave finance a real cost forecast for an AI feature instead of a monitoring promise

    • LangChain
    • Prompt Engineering
    • Vector Databases

  • Lead AI developer

    9 yrs · EU + US split

    Owns AI feature delivery across a product roadmap spanning three squads

    • Architecture
    • RAG Design
    • OpenAI API
    • Observability

Indicative ranges, representative profiles. Exact figures and named engineers arrive with your shortlist.

Weighing this against a freelancer, a direct hire or a marketplace? See the honest comparison

03From stated problem to first change

Four steps, each closing on evidence.

This sequence is built around shipping AI into your product, not a library tour. You owe us nothing if you stop at any step.

  1. Define the integration problem

    We map where AI needs to enter your product and what could break around it. The brief names that problem, not a list of AI libraries to know.

    A brief your leads sign

  2. Match against your product

    Candidates are screened against your actual stack, your data sensitivity and your review culture. Three engineers who understand your product beat thirty who list prompt engineering on a CV.

    Three fits, not thirty CVs

  3. Assess on the real tasks

    Your team can watch the RAG-construction and fallback-design exercises directly. What shows up is product judgement about AI, not model trivia.

    Evaluation you can watch

  4. Onboard inside your process

    Access, standards and a first bounded AI feature shipped for real. Continuity cover keeps a departure from ever landing on your desk unplanned.

    A merged change in week one

04Evidence and proof

Engineers who shipped AI that survived real usage.

AI feature proof is adoption that held at scale, not a demo that impressed once. Every record here waits for written client approval before production.

Explore case studies
A RAG document Q&A feature scaled to a full library.
Pillar Learning · EdTech platform, US

A document Q&A feature that scaled past the demo without a rebuild.

Pillar Learning wanted students to ask questions about course material, and the first prototype worked on ten documents but broke on the real library. An embedded AI developer redesigned the chunking strategy so it held at real scale.

Documents supported
Scaled from demo to full library
Rebuild required
None
Sample · pending client approval
Portrait of a product VP discussing AI feature cost forecasting.

Finance finally got a real cost forecast for our AI feature instead of a promise to keep an eye on it.

Anika RaoVP Product, Koru Health
Sample · pending client approval
Portrait of an operations director discussing AI fallback design.

The fallback design meant a model outage was invisible to our users instead of a broken spinner.

Mei TanakaDirector of Operations, Tern
Sample · pending client approval

05Three shapes, one standard

Add an engineer, a build pod, or a practice.

Most teams start with one engineer building a first AI feature inside the existing product. Some need a pod that owns AI capability across the roadmap. When the real ask is an AI platform delivered, not a feature bolted on, we point you to the practice instead.

  • One named engineer

    A single AI developer inside your direction and your reviews. The smallest commitment, and usually where teams begin.

    Team extension

  • A build pod

    A pod with its own lead that owns AI feature delivery across your roadmap. You set priorities while the pod carries delivery.

    Delivery pods

  • The practice, not the person

    When the real ask is an AI platform delivered, not a feature bolted on, AI & Automation carries that scope instead.

    AI & Automation

06Straight answers

Asked before an AI developer hire is signed.

Not answered here?

Ask us directly Or browse every role
What does an AI developer actually do day to day?

Integrates AI capability into an existing product, tests prompts against regressions, and designs the fallback behaviour for when an AI call fails or times out. The unglamorous majority of the work is the product decisions around the model, not the model itself.

Is this the same as hiring an AI agent developer?

Not quite. Agent development means autonomous, multi-step decision-making with its own guardrails. This role covers the broader work of shipping AI features generally, of which agent work is one specific kind.

Can we run our own interviews as well?

Yes, the published tasks are built to be reused directly. Several clients now run the RAG-construction exercise themselves in later hiring rounds.

How fast can an AI developer start?

A shortlist arrives within days from a bench evaluated continuously. Real start speed after that tracks how quickly your side can grant product and data access.

Who manages the engineer day to day?

Your leads set the roadmap and hold the review bar. What we carry is the layer behind the engineer: continuity, payroll, replacement risk and the handoff if plans change.

What if the engineer is not the right fit?

Tell us within the first weeks and the replacement is funded by us. The next engineer is briefed on the exact gap, so a repeat mismatch is uncommon.

Do your AI developers use AI coding tools themselves?

Yes, to draft boilerplate integration code and test scaffolding. The AI feature they build for you is governed separately, by the evaluation and fallback design described above.

What does an AI developer typically earn as a monthly rate?

The tier bands above are the honest starting range; the shortlist confirms the specific number per engineer once scope is clear. Monthly billing, no placement fee, notice-period exit.