Hire AI developers in 48 hours.
Our AI engineers ship LLM features that survive contact with production: RAG pipelines with retrieval quality measurement, copilots with guardrails, and document intelligence with human-in-the-loop review. We run AI workflows in production inside Garuda, our own clinic management platform — so we build for reliability and cost, not just the demo.
★★★★★ 5.0 · 21 verified Clutch reviews ↗What our AI engineers deliver.
RAG & knowledge assistants
Retrieval-augmented assistants over your documents and data — chunking strategy, vector search, citation-grounded answers.
AI copilots
In-product copilots that draft, summarise and act — with permission boundaries, audit trails and fallbacks.
Document intelligence
Extraction and classification over invoices, contracts and clinical records, with confidence scoring and review queues.
AI workflow automation
LLM steps embedded in business workflows — the pattern we run in production in Garuda's AI Workflows module.
Work our AI team has shipped.
AI Workflows module inside Garuda, our in-house clinic management platform — automating intake, documentation and follow-up tasks
Running in production clinics as one of Garuda's 8 live modules
AI-integrated iOS proof-of-concept for a property business (Blue Lotus Properties)
Validated the AI feature direction before committing to a full build
Contract review assistant extracting clauses and flagging deviations against playbooks
First-pass review time cut from hours to minutes with lawyer sign-off retained
Engineers you'll work with.
Representative profiles from our AI pool. Every engineer is CTO-reviewed before being presented to a client.
- Built a citation-grounded RAG platform over 200k internal documents with measured retrieval quality (evals in CI)
- Shipped an LLM document-extraction pipeline with confidence-based routing — 80% straight-through, the rest to human review
- Led a team of 6 to migrate a legacy monolith to microservices — zero-downtime over 4 months
- Cut TTFB by 60% on a AI platform serving 500k MAU via caching and code-splitting
* Representative composites. We send you 3 real profiles with CVs and GitHub within 24h of your request.
20+ years shipping production software. Built Kuyil AI (our AI assistant platform) and Garuda (clinic management SaaS) — along with 500+ client projects across SaaS, HealthTech, FinTech, Logistics and eCommerce.
Personally reviews every engagement’s first sprint — architecture, code quality, delivery discipline. Not a sales handoff. The CTO stays in the room.
Tell us what you need. We send you 3 hand-picked AI profiles within 24 hours.
No commitment until you choose to onboard. Interview and test before you decide.
- 1Tell us your requirementsStack specifics, seniority, timezone needs, and what you're building with AI.
- 23 profiles within 24hHand-picked engineers matched to your requirements — with CVs, GitHub profiles and sample work.
- 3Interview & testTechnical interview and optional paid test sprint before any commitment.
- 4Onboarded in 7 daysInto your standups, repos and sprint structure.
- ✓ No sales pressure✓ Reply in 24h✓ NDA available
The three pillars our pods plug into.
Hire engineers in adjacent stacks.
What an AI developer actually does in 2026
"Hire an AI developer" is four different roles in a trench coat, and hiring the wrong one is the most expensive mistake in this category. Before you screen anyone, get the title right.
The LLM application engineer builds product features on top of foundation models — RAG, copilots, document intelligence, agentic workflows — living in TypeScript/Python, API design, retrieval, evals and cost. This is who most companies asking to "hire AI developers" actually need. The ML engineer trains, fine-tunes and serves models, and owns data pipelines — the right hire when you have a modelling problem and proprietary data, not when you want a feature on top of GPT-class APIs. The data scientist frames problems and quantifies impact; essential for measurement, usually not the person who ships the production feature. The prompt engineer is a real skill, but a part of the application engineer's job rather than a standalone production hire.
The fear behind this hire is paying senior-ML salaries for someone who can only call an API — or hiring a brilliant researcher who has never shipped a feature that survives real users. Two questions disambiguate almost every brief: do you have a modelling problem or a product problem, and is the value in a model you own or in orchestrating models you rent? If the answer is "product" and "orchestrate," you want an LLM application engineer — and the rest of this page is written for that hire. It's the same framing we run on our AI application development services and in the build-an-AI-product guide.
Production AI vs demos: how we vet
Anyone can wire an impressive demo in a weekend. The gap between that demo and a feature that holds up in production — under real inputs, real cost and real failure modes — is the entire job, and it's exactly what we screen for. We anchor the bar in something concrete: we run LLM features in production ourselves, inside Garuda, our own clinic-management platform. The screen has four parts.
Retrieval quality, measured. For any RAG system, can the candidate explain chunking strategy and embedding choice — and, critically, how they measure retrieval quality rather than eyeballing it? We look for precision and recall on a labelled question set, not "it looked good in testing." Citation-grounded answers over a knowledge base are the bar, not free-floating generation.
Evals in CI. Demo-builders test by hand. Production engineers write evaluation sets that run in continuous integration, so a prompt change or a model swap can't silently regress quality. We ask candidates to design an eval harness and listen for whether they treat the model like any other dependency that needs a regression test.
Guardrails, fallbacks and PII. What happens when the model is slow, wrong or down? We screen for output guardrails, human-in-the-loop review for high-stakes actions, graceful fallbacks and PII handling designed in rather than bolted on. The principle we hire for is the one Garuda is built on: the AI layer is additive, never load-bearing — if it fails, the user works without it, not around it.
Token-cost budgeting. A senior AI engineer reasons about cost per request the way a backend engineer reasons about query plans — model routing, caching, context discipline, and knowing when not to call a model at all. Cost that's invisible in a demo is the line item that kills a feature at scale. Every candidate's pull requests are reviewed by a senior engineer; our CTO Srijith has calibrated this bar across our hire-stack pods.
What we've shipped
We field AI engineers who have shipped LLM features that real people use — not staffing rows on someone else's research project. Two proofs, both real.
Garuda AI Workflows — production LLM features in our own product
Garuda is our in-house, multi-tenant clinic-management platform, in continuous production since 2018, with eight integrated modules. One live module is AI-assisted workflows: diagnosis suggestions from symptoms and vitals, clinical-note summarisation, and triage classification for incoming patient queries. The discipline is the point. Every AI output is labelled as a suggestion, shown alongside the inputs that generated it, and dismissible in a single action — AI assists, it does not replace clinical judgment, and that's an architectural constraint, not a slogan. The AI layer has no write access to core patient records, and a model timeout, a bad output or a provider outage cannot cascade into a doctor's ability to pull up a chart. That's what "production AI" means when a silent failure touches patient care.
Blue Lotus Properties — AI-integrated iOS proof of concept
A Seattle real-estate firm needed to validate where AI genuinely added value before a full build. We delivered a focused iOS POC on time within a fixed $10k scope (5.0 on Clutch) with three LLM-powered features: inspection-report analysis that grouped issues by severity and category with a per-category risk score; property comparison that turned a natural-language investment thesis into a structured scoring model; and neighbourhood-insight synthesis over multiple sources. The part that separates production work from demos: a Node.js intermediary handling prompt construction and parsing, an output-validation layer that caught malformed responses before they reached the user, a documented prompt library tuned with domain experts, and iOS Vision-framework OCR for photographed PDFs. The client's verdict: "We were impressed by their technical abilities with AI-related concepts — they have deep understanding in this space." Read the case study →
Different domains, same engineering: measured, guard-railed, cost-aware LLM features that work outside the demo. For how this extends to a full build, see the AI application development services pillar.
Build in your cloud: data privacy by design
For most teams hiring AI developers — and for nearly every healthcare, finance or UAE buyer — the first real question isn't "how good is the model?" It's "where does our data go?" Our default answer: into your cloud, and nowhere else.
We deploy RAG and LLM pipelines inside your own AWS or Azure tenancy — Azure OpenAI or AWS Bedrock for the managed model endpoints, your vector store and application services in your account — so your documents and customer data never leave the boundary you already control and audit. Data residency is a deployment decision we make with you up front, not a setting we discover late. PII redaction and role-based access to retrieved context are part of the design from the first sprint: the model sees what it needs to answer, scoped to who is asking, and high-stakes data paths get the same human-in-the-loop review we build into Garuda.
The architecture mirrors the isolation principle from our own product: the AI service is a contained, additive component with least-privilege access, so a model integration is never a new way for sensitive data to leak. Where open-weight models are the better fit for residency or cost reasons, we run those in-tenancy too rather than forcing a hosted API.
Team composition for AI projects
You rarely need "an AI team." You need the right shape for the stage you're at — and that shape changes fast once a POC proves out.
POC shape — 1 AI engineer, ~2–4 weeks. One senior LLM application engineer to validate whether the AI actually adds value before you spend on a full build. This is the Blue Lotus shape: a lean team, a few focused use cases, a fixed scope, and a clear go/no-go at the end. The goal is evidence, not a product.
Product shape — AI engineer + full-stack engineer + designer. Once the direction is validated, the AI engineer owns retrieval, evals, guardrails and cost; a full-stack engineer owns the app the feature lives in; and a designer makes AI output legible and trustworthy in the UI. This is the smallest team that ships a real AI feature.
Scale shape — add MLOps / platform. When usage and cost grow, add MLOps for model and version management, eval pipelines, observability and inference-cost engineering — specialising the layer that has become the bottleneck while the application engineer stays on feature work.
Engagement is a dedicated, full-time monthly retainer — starting at $10k/month for a single senior engineer — with no shared-across-clients dilution and no long lock-in, the same model described on our staff augmentation page. For how AI project budgets break down by scope, see what AI application development costs.
Frequently asked questions
Can I interview developers before hiring?
Absolutely. You interview, optionally run a paid test sprint, and only commit when you're satisfied. No pressure.
What pricing models do you offer for AI engineers?
Monthly retainer for ongoing work, starting at $10k/month for a single senior engineer. Hourly for short engagements. Fixed-price for project-scoped work.
What tools do your AI developers use?
Jira, GitHub, GitLab, Linear, Slack, Notion — we adapt to your stack, not the other way around.
Are developers full-time on our project?
Yes — dedicated full-time, not shared across clients.
Can we scale up or down?
Scale up with new engineers onboarded in 48h. Scale down with two weeks notice. No contractual lock-in.
Which models and providers do you work with?
OpenAI, Anthropic, and the Azure OpenAI / AWS Bedrock managed offerings, plus open-weight models where data residency requires it. We pick per use case — and we'll tell you when a cheaper, smaller model is the right answer.
How do you handle hallucinations and quality?
Evaluation sets before launch, citation-grounded retrieval for factual answers, guardrails on output, and human-in-the-loop review queues for high-stakes actions. Quality is measured, not assumed.
Can you deploy in our cloud for data privacy?
Yes. We deploy RAG and LLM pipelines inside your AWS or Azure account, with your data never leaving your tenancy. PII redaction and access controls are part of the design, not an afterthought.
Do we need to fine-tune a model, or is RAG enough?
For the large majority of product features — answering questions over your own documents, copilots, document intelligence — retrieval-augmented generation (RAG) is the right first answer: it grounds responses in your data without the cost and maintenance of training, and it's far easier to update when your data changes. Fine-tuning earns its keep for narrow, high-volume tasks needing a specific output format or tone at lower per-call cost, and it usually comes after RAG, not instead of it. We help you make that call per use case rather than defaulting to the expensive option — see our write-up on RAG vs fine-tuning.
Which vector database do you recommend?
For most builds we start with pgvector — if you're already on PostgreSQL, keeping embeddings next to your relational data removes a moving part and scales further than people expect. We reach for a managed store like Pinecone when you need very large indexes, high query concurrency, or managed operations your team would rather not run. Honestly, the vector store is rarely the hard part; retrieval quality, chunking and evals matter far more, so we pick the simplest option that meets your scale and measure quality on top of it.
How do you keep inference costs predictable?
We treat cost per request as a first-class design constraint: model routing (a smaller, cheaper model where it performs just as well, reserving the frontier model for requests that need it), caching repeated calls, disciplined context-window usage, and eval-driven right-sizing so a cheaper configuration is a measured decision, not a guess. Because cost and quality trade off, we instrument both so you can see the effect of a change before it ships.
Can you take over a half-built AI feature from another vendor?
Yes — it's one of the more common ways teams start with us. We begin with a short assessment of the four things that decide whether an inherited AI feature is salvageable: is retrieval quality measured or assumed, are there evals or just manual spot-checks, are there guardrails and fallbacks for when the model misbehaves, and is prompt and version management documented or scattered. From there we give you a straight read on what to keep, what to rebuild, and the sequence — then embed and ship.