AI integration discovery
Clarify the workflow, model and tool choices, data access, risks, and a bounded first milestone.
AI Integration Engineer
AI features are easy to demo. I build the systems that make them dependable.
I help product teams connect LLMs, agents, data, and business APIs to reliable Python backends — so an AI prototype can become a product people trust.
- LLM & API integration
- Agentic workflows
- Python production systems
8+ yearsSoftware engineering
World BankComplex public systems
Python firstDjango · FastAPI · APIs
End to endArchitecture to deployment
What I help build
AI capability with the surrounding engineering included.
A model endpoint is not a product. I work across the workflow, application, data, safeguards, and deployment that make it useful.
Agents and workflow automation
Build tool-using workflows with explicit contracts, validation, retries, approval gates, and operational visibility.
Production AI backends
Design the Python APIs, background jobs, permissions, data flows, testing, and cloud delivery around the AI feature.
Selected work
Engineering proof, not an inventory of technologies.
Examples of existing-system work where integrations, reliability, data, and operational ownership matter.
Working principles
Make the system accountable before making it autonomous.
Start with the workflow.Use model judgment only where ambiguity creates value.
Keep actions bounded.Permissions, tool contracts, and approval gates are architecture.
Evaluate real failure costs.Test the decisions and outputs the product actually depends on.
Design for operation.Latency, cost, retries, fallbacks, and observability shape the experience.
Practical writing
Notes on moving AI from possibility to production.
7 min read
Browse all insights Production AI is a systems problem
The model call may be the most visible component, but the surrounding workflow determines whether an AI feature is dependable.
→6 min readWhen an agent is the wrong tool
Autonomy is valuable when a workflow requires judgment. It is expensive and risky when ordinary software can express the rules.
→6 min readEvaluate the workflow before you scale the AI
A useful evaluation set starts with real decisions and failure costs — not a generic benchmark score.
→Have a real workflow in mind?