How I work

How I ship reliable fullstack platforms and AI features.

Four years delivering Spring Boot/Angular platforms, Kafka-backed data flows, and applied AI/LLM features. The focus is scoped engagements with acceptance tests, weekly slices, and observable handoff so teams can run what we build.

1) Clarify scope and acceptance

We start with outcomes, constraints, and interfaces. I write down acceptance tests, integration points, and what “done” means so we avoid scope drift and overengineering.

• Success criteria: user paths, SLAs, failure modes
• Constraints: time, budget, security, data, operations
• Interfaces: contracts between frontend, backend, data, and AI services

2) Stack choices follow reliability

I pick tools based on reliability, maintainability, and handoff. Typical choices: Spring Boot for services, Angular for frontends, Kafka for streaming, Postgres/MySQL/MongoDB for storage, and Python/PyTorch/Spring AI for models and LLM features.

3) AI & ML treated as product

Neural features, classical models, and LLM flows are engineered like the rest of the system: evaluation, cost control, and guardrails from day one.

• Baselines, metrics, and backtesting for every model
• Reproducible pipelines and data quality gates
• Deployment with monitoring, tracing, and rollback plans
• LLM interfaces with Spring AI plus prompt/latency/cost guardrails

4) Delivery, observability, handoff

Weekly thin slices with demos, tests, and instrumentation. I keep assumptions explicit, document decisions, and hand off with runbooks so you are not dependent on me.

• Tests, logging, tracing/metrics where they matter
• Runbooks, dashboards, and basic alerts
• Deployment playbooks and rollback paths
• Docs and training for your team

5) Engagement modes

Freelance, delivery-focused, and scoped. I either own a mission end-to-end or embed with your team to ship specific slices.

• Mission-based delivery — scoped platform, service, or AI/LLM feature from discovery to production handoff.
• Delivery sprints — 1–4 week pushes to ship a thin slice or unblock a roadmap item with tests and observability.
• Part-time embedded — hands-on within your team, owning tasks and integrations while keeping leadership in the loop.

The goal is clear scope, visible progress, and production-ready outcomes — not open-ended advisory work.

What working together feels like

Structured, transparent, and calm. I surface trade-offs early, document decisions, keep stakeholders updated weekly, and make progress visible with demos and metrics.