01Architecture
Product & platform systems
Service boundaries, APIs, distributed workflows, data contracts, and the technical decisions that keep a product adaptable as it grows.
Backend architecture · integrations · platform design
Lead Engineer · AI & Production Systems
I build production systems that hold up.
From AI agents and backend platforms to data pipelines and technical direction, I turn ambiguous product problems into reliable software teams can own.
7 years building production softwareRemote · worldwide
A system is more than its smartest component.
Across the stack
AI systemsBackend platformsData pipelinesReliabilityTechnical leadership
01 · Expertise
One engineering practice, four connected disciplines.
AI is a specialty inside the broader work of designing, shipping, and operating dependable software.
02Intelligence
Agentic AI systems
Agents, tools, context, memory, retrieval, evals, and guardrails designed as production software rather than isolated model demos.
Orchestration · RAG · evals · safety
03Operations
Reliability & scale
Observability, performance, testing, deployment design, and failure recovery that make complex systems easier to trust and operate.
Tracing · testing · performance · delivery
04Leadership
Technical direction
Turning ambiguous product goals into engineering boundaries, sequenced plans, reviewable tradeoffs, and systems teams can own.
Scoping · decisions · mentoring · handover
Selected outcomes
Evidence from previous roles, presented without client or system-sensitive detail.
7 yearsbuilding production software across AI, platforms, fintech, and SaaS
70%API latency reduction through query and caching redesign
50%reduction in critical incidents after stronger testing and delivery practices
95%on-time delivery after setting standards across multiple product surfaces
02 · Approach
Technical leadership from ambiguity to ownership.
The work is not finished when the architecture diagram is approved. It is finished when the system runs and the team can evolve it.
Frame the real problem
Start with the outcome, constraints, and failure cost. Choose the smallest system that solves the actual operational problem.
Engineer the boundaries
Make ownership, data flow, permissions, retries, and observability explicit before complexity turns them into production incidents.
Leave a system behind
Ship with tests, operating signals, documentation, and a team that understands the decisions—not a black box only one person can change.
03 · Writing
Engineering decisions, explained.
Field notes on AI systems, production architecture, reliability, data, and the judgment behind technical tradeoffs.
Deletion across data systems is a workflow, not a query
Coordinate erasure through an idempotent, checkpointed workflow when one subject's data spans operational, analytical, and AI stores.
How to measure the real cost of an agent evaluation
A practical accounting model for judge calls, agent calls, retries, and the unattributed work that per-case dashboards often hide.
Version the policy and evaluator separately
Record decision rules independently from measurement logic so AI behavior can be replayed, compared, and audited honestly.
Make your evaluation harness run the agent you ship
Use the production context path with synthetic data, relative dates, and structural fixtures so an eval measures real behavior without copying private state.
04 · About
Broad systems thinking, grounded in delivery.
I have spent seven years building and leading production software across AI products, decentralized infrastructure, fintech, aviation, and SaaS—from backend architecture and behavioral-data pipelines to agentic systems and the safeguards that let them act reliably.
My work sits where product ambiguity becomes technical commitment: defining boundaries, choosing tradeoffs, sequencing delivery, and making sure the result remains understandable after launch.
The common thread is not a particular technology. It is building systems that survive contact with real users, real data, and real operational consequences.
05 · Contact
Need a stronger technical path through a complex product?
Tell me what you are building, where it is stuck, and what reliable delivery needs to look like. I will tell you where I can help.