Senior EngineeringAI‑Augmented Delivery
I work as a senior developer or fractional tech lead inside the existing delivery process. That gives me the context to contribute directly, make better technical decisions, and help the team introduce AI workflows where they create practical leverage.
CAREER SPAN
15years
Senior engineering experience
SCALE
100K+users
Production systems delivered
SCOPE
Full stack
Backend, frontend & technical leadership
I start with the real environment: the codebase, architecture, delivery pressure, team responsibilities, and current tooling. The goal is to understand before changing anything.
I take ownership of real engineering work — implementation, architecture decisions, reviews, production issues, and the technical conversations needed to keep delivery moving.
While working with the team, I identify where better tooling or a structured AI workflow could reduce repetition, shorten feedback loops, or strengthen review and documentation.
Useful patterns are documented and adapted to the team. The objective is not dependence on a consultant or a specific tool, but a workflow the team understands and can continue to refine.
I contribute across backend and frontend, with particular experience in Java, Spring, JavaScript/TypeScript, React, APIs, authentication, and production delivery. I am most useful where a team needs someone who can move between implementation details and the broader architectural picture.
The role is hands-on. I write code, investigate failures, review changes, and take responsibility for getting work safely into production.
Some teams need experienced technical direction but not another full-time management layer. As a fractional tech lead, I combine delivery work with architecture decisions, code review, mentoring, technical planning, and communication with stakeholders.
Because I remain close to the code, technical direction stays connected to what the team is actually building.
I have developed a structured multi-agent workflow for my own production work. AI agents can assist with research, implementation, tests, review passes, refactoring, and documentation. I remain responsible for architecture, security, correctness, and deciding what ships.
When I work inside a team, I can help introduce the parts of that workflow that fit the environment. That might mean improving prompts and context, adding independent review, capturing recurring lessons, strengthening test feedback, or removing repetitive handoffs.
We start from real delivery friction and introduce only the practices that make the work better.
My AI workflow is grounded in my own production work. Within developer or tech lead engagements, I adapt the useful parts to the team's actual environment and delivery needs.
AI can produce a plausible answer before it has understood the problem. Experience matters most at that boundary.
Device changes from a mobile app were not reaching the central unit.
The agent found missing sync logic, wrote a fix, and added a test — but the test failed.
After several attempts, the agent concluded that the test helper was unreliable and the code could still ship.
I refused to treat the failing test as an obstacle. A second review exposed an ordering bug: sync messages were sent before the database transaction committed.
The implementation was corrected, the test passed, a production incident was prevented, and the lesson was stored for future sessions.
AI handled the volume. Experience made the judgment call.
The best way to understand this workflow is to see it running. The case study on this site was produced by the same system it describes — Hermes writes, Claude reviews, I decide what ships. The tooling, the process, and the failure modes are all documented as we go.
