Python · OpenAI API · LangChain · pgvector
Hire AI engineers
Vetted AI engineers who build LLM features, copilots, AI agents and RAG into your product and take them from prototype to production, matched to your use case and budget. On your team in 2–3 weeks.
Rated 5.0 on Clutch · CVs in 3–5 days · No long-term lock-in
- Python
- TypeScript
- OpenAI API
- Anthropic API
- LangChain
- pgvector
What does an AI engineer do?
An AI engineer builds product features on top of large language models (LLMs) and other foundation models: assistants and copilots, AI agents that use tools, and retrieval-augmented generation (RAG) over company data. Instead of training models from scratch, they choose the right model, connect it to your data and systems, and add evaluation and guardrails so the feature is accurate, safe and affordable to run in production.
DevKit AI engineers work in Python and TypeScript with the OpenAI and Anthropic APIs, open-source LLMs, LangChain or LlamaIndex, and vector databases such as pgvector, Pinecone and Weaviate. Our engineers have built AI platforms for clients in cybersecurity and insurance, and every one of them passes a technical interview with our senior engineers before you see their CV.
When to hire one
- Adding LLM features, copilots or chatbots to your product
- Building RAG search and Q&A over your documents and data
- Automating multi-step workflows with AI agents and tool use
- Moving LLM prototypes into reliable, evaluated production features
What our AI engineers do
Typical work our AI engineers take on for clients.
LLM applications and copilots
Chat, summarisation, extraction and in-app copilot features built on LLM APIs with structured outputs and prompt controls.
AI agents and tool use
Agents that call your APIs and tools to complete multi-step tasks, with permissions, guardrails and human review where needed.
Retrieval-augmented generation (RAG)
Search and Q&A over your documents with embeddings, vector databases, hybrid search and source citations.
Prompt and evaluation pipelines
Versioned prompts, test sets and automated evaluations that show whether a model or prompt change actually helps.
Guardrails and safety
Input and output filtering, PII handling and fallbacks that keep LLM features predictable and compliant.
Model integration and cost control
OpenAI, Anthropic and open-source models integrated behind your APIs, with caching, routing and usage monitoring.
AI technologies we cover
Languages and frameworks
- Python
- TypeScript
- Node.js
- FastAPI
- LangChain
- LlamaIndex
Models and APIs
- OpenAI API
- Anthropic API
- Azure OpenAI
- Open-source LLMs
- Hugging Face
RAG and vector search
- pgvector
- Pinecone
- Weaviate
- Elasticsearch
- PostgreSQL
Deployment and cloud
- Docker
- Kubernetes
- AWS
- Google Cloud
- Azure
Matched to your budget: which level do you need?
We match the level to the work, so you don't pay senior rates for tasks a mid-level engineer handles well, or put critical work on the wrong hire.
Junior
Well-defined tasks with guidance
Bug fixes, tests and smaller features in an established codebase, with a senior on your team to review their work. The most budget-friendly option.
Mid-level
Independent feature work
Builds features end to end in an existing architecture with little supervision. The best value for most product teams.
Senior
Complex features and ownership
Owns complex parts of the system, makes sound technical decisions and mentors others. Worth it where mistakes are expensive.
Tech lead / Architect
Architecture and team leadership
Designs the system, sets standards and leads other engineers. Ideal for new products, rewrites or a team without technical leadership.
Not sure? Tell us the work and your budget on a free call, and we'll recommend the right level for each role.
What's in every profile we send
Within 3–5 business days of our call, you get a shortlist of vetted candidates. Every profile gives you what you need to decide quickly.
- 01
Skills verified by engineers
Each candidate passes a technical interview with our senior engineers. You see what they can actually do, not just what their CV says.
- 02
Interviewer's notes
Strengths, gaps and how the candidate approached the interview tasks, written by the engineer who interviewed them.
- 03
Why this match
How the person fits your role, your team and your budget, and which level we recommend for the work.
- 04
Level and relevant experience
From junior to tech lead, with the projects and domains that matter for your role.
- 05
English, time zone and availability
Communication level, working-hours overlap with your team and the earliest start date.
- 06
A clear monthly rate
One price per engineer per month, so you can compare candidates against your budget straight away.
Client reviews: rated 5.0 on Clutch
Verified Clutch reviews from engineering leaders and founders who scaled their teams with DevKit.
Head of Engineering, IT Company
“Their flexibility and ability to provide access to talented specialists.”
Read on ClutchLead Frontend Engineer, Ultra Commerce
“We were particularly impressed by their management approach.”
Read on ClutchSenior Unity Engineer, Software Company
“Their management side always cared about the engineers provided.”
Read on ClutchCEO, Software & AI Company
“Their responsiveness and the quality of the engineers were impressive.”
Read on ClutchDevOps Engineer, Ubiqube
“They were always ahead of schedule and suggested a lot of modern solutions.”
Read on ClutchSenior Software Engineer, HealthTech Firm
“They combine technical expertise with a strong understanding of business objectives.”
Read on ClutchTeam Lead, AI Solutions Co
“DevKit has found an approach that works well with startups.”
Read on ClutchThree ways to hire
Choose how you want to work. You can switch models as your needs change.
Hiring AI engineers: FAQ
How quickly can I hire an AI engineer through DevKit?
You usually receive vetted AI engineer CVs within 3–5 business days, and the engineer typically starts within 2–3 weeks of the first call.
Which AI technologies do your engineers use?
Mainly Python and TypeScript with the OpenAI and Anthropic APIs and open-source LLMs, LangChain or LlamaIndex for LLM apps and agents, vector databases such as pgvector, Pinecone and Weaviate for RAG, and FastAPI or Node.js with Docker on the major clouds for deployment.
What is the difference between an AI engineer and an ML engineer?
An AI engineer builds product features on top of existing foundation models such as LLMs, using prompts, RAG, agents and evaluations. An ML engineer trains, deploys and maintains custom machine learning models on your own data. If you need an LLM feature or AI agent, hire an AI engineer; if you need a predictive, ranking or computer vision model, see our ML engineers.
Do I need a senior AI engineer?
Not always. If your AI architecture is set and someone senior reviews the work, a mid-level engineer often delivers the same results for less, for example extending an existing RAG pipeline or LLM feature. To design a new AI system, choose models and evaluation methods, or put AI agents into critical workflows, a senior engineer or tech lead is usually worth it. We'll recommend the right level on a free call.
How much does it cost to hire an AI engineer?
Staff augmentation is billed as a monthly rate per engineer, depending on the level and specialisation. Tell us your budget and we'll match AI engineers to it; exact rates come with the first CVs.
Need engineers next month? Let's talk this week.
Send us the role. Get CVs in 3–5 days.
Tell us what you need. We reply within one business day.
What happens next
- 1
We reply within one business day
A real person reads every request, not a bot.
- 2
A free 30-minute call
We talk through the role, your team, the timeline and the budget.
- 3
Vetted CVs in 3–5 days
Each profile comes with interview notes and a clear monthly rate.
“Their responsiveness and the quality of the engineers were impressive.”
hi@devkit.agency