Vetted engineers with verified commercial track record in AI Native Developer
London, United Kingdom (London, United Kingdom)
Lead AI Engineer at The Evidence Company | Agentic AI, LLM Systems, Production RAG & Knowledge Graphs
Lead AI engineer with over 10 years of experience in machine learning, data engineering, and agentic systems. Leads the technical direction and AI platform architecture at an Imperial College London spin-off (The Evidence Company). Designed from scratch an agentic knowledge-graph and RAG system for translating and analyzing large texts with Neo4j, LangChain, and vector embeddings. At Builder.ai, automated requirements-based documentation generation with NLP/LLM for 500+ enterprise projects. Author of open-source AI projects (MoM LLM for parallel multi-model orchestration, learners-mcp built on Model Context Protocol). Specializes in quality evaluation (evals), schema validation, hallucination guardrails, and optimizing LLM call costs.
London, United Kingdom (London, United Kingdom)
Software Engineer (AI & Full Stack) | React · Node.js · FastAPI · Python | LangChain · RAG · Agents | PyData London & LangChain London Speaker
Full-stack AI engineer with a master's degree in Computer Science (Machine Learning & AI specialization, GPA 4.0). Builds the full AI product stack: from React frontends and FastAPI/Node.js backends to RAG pipelines and production multi-agent systems. Worked as an engineer at DXC Technology and Alankrita, designing agent orchestration architecture, evaluation (evals) frameworks, and context engineering. Regular speaker at the PyData London and LangChain London communities on production AI agent reliability.
London, England, United Kingdom (London, United Kingdom)
AI Engineer | Forward Deployed Engineer | RAG Systems · AI Agents · LLMOps | Python · LangGraph · AWS | Ex-ThoughtWorks
AI engineer with a background in building highly reliable distributed systems (4+ years at ThoughtWorks). Specializes in building agentic systems with LangGraph, RAG pipelines, and serverless AI infrastructure on AWS Bedrock (Claude Haiku/Sonnet). At Elara, built an LLM-based enterprise communications analytics platform, processing over 94K messages through a 6-stage GenAI pipeline with structured JSON parsing and statistical normalization.
AI native developer is an editorial description for a software engineer who uses AI throughout the delivery process and can also build products whose workflows depend on language models, agents or intelligent automation. It is not a regulated title or a guarantee of a particular toolset.
When you hire an AI native developer, describe the business decision or user task that should improve. A good brief names the data the system may access, the actions it may take, where a person must approve a result and how an incorrect answer should be detected.
AI tools can accelerate scaffolding, debugging, test generation and developer automation tools, but generated code still needs review. An AI native developer should understand the existing architecture, keep changes reproducible and verify important behaviour with tests rather than treating a plausible response as evidence.
Ask how the developer will protect secrets, review generated dependencies, handle private source code and keep the repository understandable to the rest of your team. The useful advantage is a faster feedback loop that remains accountable to acceptance criteria.
An AI agent developer may connect a model to tools such as a database query, a ticket system or an internal API. The production boundary matters: each tool needs a narrow contract, authorization, input validation, timeouts and a clear record of what happened. A human approval step may be required before an irreversible action.
For RAG and other LLM applications, scope the document sources, ingestion process, chunking, retrieval, citations and evaluation examples. A demo that answers one question is not enough to establish retrieval quality, privacy or safe behaviour across the cases your customers will submit.
A generative AI developer can help move a prototype toward a service that your team can operate. The work may include a backend API, a queue for long-running jobs, observability, feedback capture and a release process that allows prompts and model versions to be reviewed safely.
Do not evaluate the result only by how impressive a handful of answers look. Agree what quality means for your domain, which failures are unacceptable, how users can correct the system and who owns the data, prompts, evaluation set and infrastructure after handover.
Include the current product state, the target users, representative inputs, systems that must be connected and the actions the AI may or may not perform. Say whether you need an AI agent, a retrieval feature, an assistant inside an existing product or AI-assisted development of a conventional application.
Request a short technical plan with risks, testable milestones and a handover outline. Rates, availability and relevant delivery experience must be confirmed with the individual developer; this page does not make claims about a specific candidate.
A direct OpenAI GPT-4o or Anthropic Claude 3.5 Sonnet call with JSON schema validation fits classification, drafts, and summaries over small context. A LlamaIndex RAG pipeline over pgvector 0.7 or Pinecone fits questions grounded in your docs. A LangChain agent with two or three narrow tools fits ticket triage and lookup-then-act flows. A small hosted Llama 3.1 8B on vLLM or Ollama fits high-volume, low-margin inference. Independent rates run $45-$95/hr against $150-$250/hr at agencies, with matching in 2-4 hours.
Expect 10-20 hours for a validated direct-API feature with streaming and cost caps, 25-50 hours for a RAG service with ingestion, chunking, citations, and 50 graded examples, 30-60 hours for an agent with approvals and audit logs, and 20-40 hours for a hosted small-model endpoint with fallback to a frontier API. Pick direct API for speed, RAG for grounded answers, agents for multi-step acts, and small models when per-token bills dominate.
| Option | Cost / time | Limits | Pick when |
|---|---|---|---|
| Direct GPT-4o / Claude API | $45-$95/hr, 10-20 hrs | Hallucinates past context | Drafts and labels with validation |
| RAG with pgvector/Pinecone | $55-$95/hr, 25-50 hrs | Needs ingestion upkeep | Answers must cite your docs |
| LangChain agent + tools | $60-$95/hr, 30-60 hrs | Tool failure surface | Lookup-then-act with approval |
| Llama 3.1 8B on vLLM | $55-$95/hr, 20-40 hrs | Weaker reasoning | High volume, tight token budget |
Failed pilots usually show prompt strings in frontend code, a 200k-token context stuffed per request, no evaluation set, and a Postgres table of pasted answers nobody grades. Phase one (4-8 hours) caps spend with per-key budgets, adds request logging with hashed PII, and builds a 50-case graded set from real tickets. No model swap ships before the graded set runs green on the current baseline.
Phase two (15-30 hours) rebuilds ingestion with fixed chunk sizes in LlamaIndex, pgvector indexes, citation rendering, and a queue with BullMQ for long jobs. Phase three (8-15 hours) adds approvals for irreversible acts, fallback to a second provider, and Grafana dashboards for latency, refusal rate, and cost per task. Old prompts stay live behind a flag until graded quality holds for two weeks.
Migration guardrail
Cap tokens and log every tool call before tuning quality. Without spend limits and traces, a cleanup sprint can burn a month of API budget in a weekend.
A takeover starts with repository access, provider invoices for the last 60 days, the prompt and tool registry, vector-store index stats, and ten real failure transcripts. A developer spends 4-6 hours checking key scoping, tenant isolation, chunk overlap, top-k settings, and approval gaps before irreversible acts. You receive a keep-versus-rebuild verdict with hour bands before tuning begins. The verdict lists files, prompts, and first-sprint hours.
The audit separates prompt tweaks from architecture faults: missing citations, cross-tenant context leaks, unbounded tool loops, and no offline evaluation. Repair scopes of 15-35 hours fit pipelines with sound ingestion. When every answer bypasses retrieval and keys sit in client bundles, rebuilding the service boundary costs less than patching prompts. That call uses your graded scores, invoice totals, and trace samples.
| Signal in audit | Meaning | Response |
|---|---|---|
| Keys in frontend bundle | Full account exposure | Rotate, proxy server-side, 4-8 hrs |
| No graded examples | Quality is opinion | Build 50-case set, 5-10 hrs |
| Tools without approval | Irreversible side effects | Add approval gate, 6-12 hrs |
| Flat accuracy, high spend | Wrong architecture | Rebuild retrieval cheaper than tuning |
Rebuild threshold
If grounding, isolation, and evaluation repair exceeds 60% of a fresh RAG build estimate, approve a clean rebuild and import only proven prompts and graded cases.
Screen for shipped LLM features with evaluation numbers: retrieval hit rate, citation precision, p95 latency, and cost per thousand tasks on GPT-4o or Claude 3.5. Ask how they set chunk size and top-k in pgvector, how they test a tool refusal, and where tenant IDs are enforced in the retrieval filter. Seniors cite graded deltas and incident fallbacks. Juniors describe demo prompts without metrics, isolation, or budget detail.
Run a paid 3-5-hour trial on sanitized data: add grounded citations to five answers, write ten graded cases with pass marks, or gate one tool behind human approval with an audit log. Acceptance means tests green, traces viewable in LangSmith or logs, cost per task recorded, and hours logged. Matching takes 2-4 hours; independents bill $45-$95/hr versus $150-$250/hr at agencies.
It is an editorial term for a software developer who uses AI tools throughout delivery and can build AI-first product workflows. It is not a formal certification, so evaluate the person's relevant code, architecture decisions and production practices.
Choose by the work. An LLM developer may focus on model integrations and retrieval, while an AI native developer may also cover the surrounding product and use AI-assisted engineering. A project can need both capabilities in one person or across a team.
Yes, if the scope includes authenticated tools, least-privilege access, validation, logging and a safe approval path. Do not share credentials in a public brief; arrange private access after agreeing the scope.
Ask for representative evaluation cases, failure handling, privacy boundaries, cost and latency assumptions, monitoring and a rollback plan. The exact checks depend on the decisions the application supports.
There is no single rate for AI work. Request an estimate that separates discovery, application engineering, model or provider costs, evaluation, deployment and handover. Confirm the developer's rate directly.
Choose a direct GPT-4o or Claude call for drafts with validation (10-20 hours), RAG with pgvector for cited answers (25-50 hours), and an agent only for lookup-then-act flows needing approval (30-60 hours). A 4-hour technical plan settles the fit.
10-20 hours for a validated API feature, 25-50 hours for a cited RAG service with 50 graded cases, 30-60 hours for an approval-gated agent, and 20-40 hours for a hosted Llama 3.1 endpoint. Timelines follow a 4-6-hour audit of data and tools.
Yes, with repo access, 60 days of provider invoices, prompt and tool lists, and ten failure transcripts. A 4-6-hour audit checks isolation, retrieval settings, and approvals, then gives a keep-versus-rebuild verdict.
Give a paid 3-5-hour task on sanitized data: add citations to five answers or gate one tool behind approval with logs. Pass means graded cases green, traces stored, cost per task noted, and hours logged.
Provide representative inputs, document sources, API access paths, forbidden actions, approval owners, and privacy limits. Share keys privately after scope sign-off; never place secrets or customer data in the brief.
Agree 4-8 hours per month at $45-$95/hr for graded checks, prompt versioning, index refreshes, and latency or spend review, with named response windows for grounding failures. Provider bills stay listed separately.
