AI Engineering

Production AI.
Not pilots that stall.

Senior engineering for teams shipping AI into real products. Agent architecture, MCP servers, RAG, evals, and integration into the Next.js and Node.js stack you already run. Three years deep on top of twelve in senior delivery.

What I deliver

Agent Architecture

Multi-agent orchestration with planner, executor, and critic patterns. Tool calling, structured outputs, retries with exponential backoff. Built for production traffic, not demos.

MCP Server Design

Custom Model Context Protocol servers exposing your internal systems to AI clients. Auth, rate limiting, observability, and tested capability surface.

RAG Architecture

Retrieval pipelines that ground answers in your data. Hybrid search, reranking, citation-first responses, and chunking strategies that survive document churn.

Eval Frameworks

Test suites that catch regressions before they ship. Golden datasets, LLM-as-judge with calibration, A/B harnesses for prompt and model changes.

Durable Workflows

Long-running AI pipelines that survive restarts. Step-based execution with retries and idempotency for jobs that take minutes or hours.

Token Economics & FinOps

Cost guardrails per tenant or per role. Caching layers, prompt compression, and model routing that keep margins intact at scale.

Production Integration

AI pulled into your existing Next.js, Node.js, or monorepo stack. Streaming responses, optimistic UI, and a clean separation between AI and business logic.

Model Selection & Routing

Pragmatic choices between OpenAI, Anthropic, Google, and open-weight models. Routing logic that picks the right model per task without lock-in.

Observability & Audit

Trace every prompt, every token, every tool call. Linked to user sessions and stored where compliance can find it.

Where this fits

Most engagements fall into one of these shapes. Custom scope on request.

AI inside your existing app

You have a Next.js or Node.js product. You need AI features that ship without slowing the rest of the team. I integrate the model layer, design the prompt and eval surface, and hand off code your engineers can own.

Agent platform for revenue ops

Multi-agent system for lead enrichment, account research, or proposal generation. Tool calling against CRMs, document stores, and internal APIs. Pricing and scope set per use case.

MCP server for an internal data platform

Expose your data to Claude Desktop, IDEs, or custom AI clients. Auth, rate limits, and a tested capability surface that doesn't break when the model changes.

Migration off a stalled pilot

Pilots that worked in a notebook but don't survive production traffic. I rewrite the AI layer with proper retries, evals, and cost controls so the launch ships.

Stack I work in

OpenAI APIAnthropic ClaudeVercel AI SDKLangChainLangGraphMCP ProtocolLlama 3QwenMistralvLLMOllamaPineconeWeaviatepgvectorPostgreSQLNext.jsNode.jsTypeScriptPythonDockerKubernetesAWS Lambda

Engagement

Ready to ship production AI?

Tell me where the AI work sits today and where it needs to be. Discovery call to scope, or a written brief if you prefer.

AI engineering engagements start at $18k.

Agent platform builds, MCP servers, and production AI integration. Day rate from $1,800 for embedded work. Most engagements run 4 to 12 weeks.

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