AI & LLM Engineering
Production AI features, not demos that fall over
Retrieval-augmented generation, tool-using agents, and reliable LLM features wired into real products, with the evaluation, guardrails, and cost control that keep them shipping.
Anyone can wire up a chat box against an API key in an afternoon. The hard part, the part you actually hire a senior engineer for, is making an AI feature reliable, grounded, fast, and safe enough to put in front of paying customers. I build LLM features that hold up in production: retrieval-augmented generation over your own data, agents that call tools and take actions, and structured outputs your app can trust instead of parse-and-pray.
Most AI projects I am brought in to rescue fail in the same predictable ways: retrieval that returns the wrong chunks, so the model confidently hallucinates; no evaluation, so nobody can tell whether a prompt change made things better or worse; prompt injection and untrusted output handled as an afterthought; and a bill that scales linearly with traffic because nothing is cached or right-sized. I treat those as first-class engineering problems, not prompt tweaks, because that is what they are.
Seventeen years of building full-stack systems means I see AI as one component of a real product, not a magic box. The model is the easy part; the data pipeline, the retrieval quality, the eval harness, the caching layer, the security boundary, and the integration with your existing stack are where the work actually is. You get an engineer who can build the RAG pipeline, the Postgres schema, the API, and the frontend it lives in, so the AI feature ships as a coherent product rather than a bolted-on prototype.
What you get
Deliverables
RAG pipeline
End-to-end retrieval, ingestion, chunking, embeddings, hybrid retrieval and reranking, tuned for your data and grounded with citations.
LLM feature or agent
A production chat, assistant, or tool-using agent integrated into your app with streaming, structured outputs, and safe tool execution.
Evaluation harness
A golden dataset and automated evals (including LLM-as-judge) wired into CI so prompt and model changes are measured, not guessed.
Guardrails & security
Prompt-injection defenses, least-privilege tools, output handling, and PII controls grounded in the OWASP LLM Top 10.
Cost & latency tuning
Prompt and semantic caching, model routing, and right-sizing that cut per-request cost and p95 latency, with the numbers to prove it.
Handoff & docs
Architecture notes and runbooks so your team can operate, evaluate, and extend the AI feature confidently.
Stack
Technologies I use for this
How it goes
The engagement
Scope & feasibility
We pin down the use case, the data, and what 'good enough' means, then I tell you honestly whether an LLM is the right tool and where it will struggle.
Retrieval & data
I build the ingestion and retrieval layer first, because in most AI features retrieval quality, not the model, decides whether the output is any good.
Build & evaluate
I ship the feature behind an eval harness so every prompt, model, and retrieval change is measured against real cases before it goes live.
Harden & optimize
I add guardrails, prompt-injection defenses, caching, and cost controls, then hand over docs so your team can own it.
FAQ
Questions about AI & LLM Engineering
- Can you build RAG over our own private data?
- Yes, that is most of what I do. I build the full retrieval pipeline over your documents or database, keep it in sync, and ground the model's answers with citations so it stops making things up.
- Which models and providers do you work with?
- Primarily OpenAI and Anthropic Claude, directly or through the Vercel AI Gateway, plus open models where they fit. I am provider-agnostic and usually set up model routing so you can switch or fall back without rewriting the app.
- How do you keep LLM features reliable and stop hallucinations?
- Strong retrieval so the model has the right context, structured outputs validated with Zod so the app never parses free text, and an evaluation harness so regressions are caught in CI. Grounding plus evals is what turns a demo into something you can trust.
- Is an AI agent the right fit for our problem?
- Often not, and I will tell you so. Many 'agent' use cases are better served by a fixed chain or a single structured call, which is cheaper and far more predictable. When you genuinely need an agent loop, I build it with strict guardrails, step and cost caps, and safe tool access.
More services
Explore the rest of the stack
Frontend Development
Fast, accessible interfaces in React, Next.js and TypeScript.
Backend & APIs
Reliable services, clean REST/GraphQL APIs, sane data models.
Database Design & Optimization
Schemas that scale and queries that stay fast.
SaaS Platforms
Multi-tenant products built to scale predictably.
E-Commerce Solutions
Custom storefronts and headless commerce that convert.
Cloud & DevOps
CI/CD, containers, and infrastructure that ships safely.
Need help with AI & LLM Engineering?
Tell me about your project and I'll tell you honestly whether I'm the right fit.
Get in touch