09
AI Platform Engineering
End-to-end AI platform work — not just an API call to OpenAI behind a chat widget. Data pipelines, model selection or fine-tuning, evaluation, and the MLOps to keep it from regressing in production.
What this looks like
Models & fine-tuning
- Foundation model selection (open + closed weights) by task fit and cost
- Fine-tuning, LoRA, and instruction-tuning where it earns its keep
- Classical ML and neural-net models when LLMs are the wrong tool
Pipelines & RAG
- Data prep, embedding, and vector store design
- Retrieval-augmented generation with chunking + reranking
- Agent orchestration with tool use, memory, and guardrails
MLOps & evaluation
- Eval harnesses with regression tests on prompt + model changes
- Tracing, observability, cost attribution per request
- Model promotion gates and rollback paths
Tools & tech
Python
PyTorch
LangChain
LlamaIndex
Vercel AI SDK
Pinecone / pgvector
Vercel AI Gateway
More services
View all →Fractional CTO Leadership
Embedded as your part-time CTO. Own technical direction, unblock the team, and report into the board.
Fundraising & Deal Support
Pre-seed to seed fundraising from the founder's side of the table. Legal, financial, and technical workstreams run in parallel — so you close faster and cleaner.
Product Management
Ship the right features at the right time. Roadmaps tied to real user outcomes, not founder instinct.
Ready to scale your engineering?
Book a 30-minute discovery call. If we're not a fit, I'll tell you on the call — and point you toward someone who is.