AI Developer for Hire — Custom Models, RAG Systems, LLM Pipelines

What I build as an AI developer

In business terms: I build the AI that actually does the work — not a chatbot bolted onto your site, but a system trained on your data, running on your hardware (or in your cloud), with the same audit trail and reliability you expect from the rest of your software. Here is the technical breakdown:

• Custom self-models. An AI model trained on your business's own documents, transcripts, or product data — so it gets your terminology right and stops hallucinating. LoRA / QLoRA / full fine-tunes on the buyer's corpus, with reproducible runs and published eval scores before promotion.
• RAG retrieval systems. AI that quotes its sources — answers come with citations back to the document they came from, and your team can audit every answer. Vector databases (pgvector, Qdrant, Weaviate), citation-first answers, versioned source documents, audit trails.
• Agent workflows. AI that can take actions in your business systems — looking things up, drafting documents, updating records — with logs of every decision. Tool-use loops, structured-output pipelines, eval harnesses for agent decisions, observability for production runs.
• Self-hosted deployment. The model runs on your own servers, not OpenAI's — so customer data never leaves your network and you pay per server, not per question. llama.cpp, vLLM, Ollama, or Triton — whichever the buyer's hardware and latency target justify.
• LLM monitoring + observability. Production AI needs the same monitoring as the rest of your software — dashboards that warn you when answers start drifting, costs spike, or quality drops. Drift detection, golden-set regression suites, A/B and shadow deployment, cost guardrails.

AI developer vs AI consultant — the honest line

An AI consultant produces a roadmap, a build/buy/wrap matrix, and an executive briefing. An AI developer produces a running system. Many engagements need both — I do both — but they are different deliverables with different success criteria. If you need a strategy document, the AI strategy consulting page is the right entry. If you need a working AI system, this page is.

Self-model vs wrapper — what changes the engagement

Most "AI" engagements buyers see today are wrappers — a vendor's API call inside a thin UI. Wrappers ship fast and cost per token. Custom self-models cost more up-front and zero per token after deployment. Each is correct in different shapes: the wrapper is right when the buyer is validating a hypothesis; the self-model is right when the buyer has a known workflow and is paying $20K+/month in per-token fees. The honest matrix is on the customer-service AI page.

Who hires me as an AI developer

VPs of engineering whose teams have a Python ML stack but need a senior who has shipped LLM production systems. Founders building AI-native SaaS who need someone who has done the fine-tuning + eval-harness + monitoring loop. Regulated-industry CTOs (healthcare, energy, financial services) whose compliance posture rules out the SaaS-API path and requires self-hosted models. Microsoft-shop enterprises that want a custom AI workflow without a Vercel / Replicate / OpenAI dependency in production.

How an AI engagement is scoped

Same disciplined shape as my Laravel / PHP engagements, with one addition: the eval harness is sprint zero. Before I write a fine-tuning script or a RAG ingest pipeline, the engagement produces an eval harness with a golden set and a measurable success criterion. If we cannot measure it, we cannot ship it.