RAG systems that earn trust
Retrieval design, document pipelines, grounding, citations, evaluation datasets, observability, and failure paths for knowledge-intensive products.
- RAG architecture
- Evaluation harnesses
- Search and retrieval quality
Freelance AI engineer · Available worldwide
AI engineering for the distance between prototype and production.
I help teams design, build, and harden generative AI products—connecting RAG, agents, evaluation, security, full-stack software, and cloud delivery in one accountable engineering loop.
01 / SERVICES
Specialist depth.
Full-product range.
Focused AI work succeeds when the model, product, data, security, and operations are designed as one system.
Agents built for real operations
Stateful workflows with tool use, human handoffs, authorization boundaries, recovery policies, and the controls needed beyond a demo.
- Agent state machines
- Human-in-the-loop flows
- Tool and permission design
AI-native product delivery
A connected path from product discovery and prototyping to Next.js interfaces, APIs, data models, cloud deployment, and production monitoring.
- Product prototyping
- Full-stack implementation
- AWS delivery
02 / WORKING MODEL
A practical path from
ambiguity to evidence.
Engagements stay focused on the riskiest decisions first, then widen into implementation as the evidence improves.
Frame the outcome
Clarify the user decision, available evidence, risk, and what success looks like before choosing a model.
Prove the hard part
Prototype the riskiest workflow with representative data and an evaluation approach, not only a polished happy path.
Build the product system
Connect the model layer to the interface, services, permissions, state, observability, and recovery paths.
Launch and improve
Ship with measurable quality signals, document operational decisions, and refine from real usage.
03 / COMMON QUESTIONS
Before we start.
What kinds of AI projects are the best fit?
Products that need RAG, agentic workflows, LLM evaluation, secure AI integrations, or a senior engineer who can own both the AI layer and the surrounding full-stack system.
Can you improve an existing AI prototype?
Yes. A typical engagement starts by identifying where the prototype breaks under real data, ambiguous requests, tool failures, security constraints, or operational scale.
Do you work only on the model layer?
No. My advantage is connecting AI architecture to product UX, backend services, data, cloud infrastructure, analytics, evaluation, and launch.
How does a freelance engagement begin?
We start with the business outcome, current system, constraints, and riskiest assumptions. From there I recommend a focused discovery, prototype, implementation, or technical audit.
04 / NEXT STEP
Let’s find the highest-leverage first move.
Share the product, constraints, and where the current approach stops working.