Fullstack · AI Engineering
I build end-to-end web and AI products—APIs, frontends, streaming backbones, and neural features—using Spring Boot, Angular, Java, Python, PyTorch, and Spring AI. Four years shipping scoped projects with contracts, thin slices, and observable releases.
Fullstack delivery
Spring Boot · Angular · APIs · Contracts
Systems & streaming
Kafka · Postgres · MySQL · MongoDB
AI engineering
Python · PyTorch · Neural networks · Spring AI
1) Align
Contracts, acceptance tests, dashboards to land.
2) Build
Thin slices shipped weekly with observability.
3) Handoff
Runbooks, alerts, and walkthrough for your team.
Delivery
4 years · 15+ launches
Fullstack platforms and applied AI features in production.
Engagement rhythm
Weekly slices
Thin vertical cuts, demos, and instrumentation every week.
Handoff
Dashboards · Runbooks
Docs, alerts, and training so teams can operate confidently.
Services
Fullstack builds and AI features delivered as scoped engagements with milestones, tests, and operational handoff.
Fullstack & APIs
APIs, frontends, auth, and async workflows with reliability patterns (idempotency, retries, DLQs). Designed for maintainability, performance, and handoff.
Streaming & data systems
Streaming and storage backbones with quality gates and monitoring. Comfortable with Kafka, SQL/NoSQL stores, time-series, and observable pipelines.
Applied AI & ML
Neural features, evaluation/backtesting, and production deployment. Includes classical prediction, LLM-powered flows with Spring AI, and monitored rollouts.
Selected work
Fullstack and AI projects shipped as thin slices with clear milestones, production readiness, and handoff.
Consultations platform with routing, notifications, and payments. Built for reliable booking flows, clear failure handling, and observable services.
AI-assisted app generation with secure auth and payments. Integrated LLM capabilities with Spring AI and hardened the platform for production use.
Adaptive learning platform with dynamic scheduling, instructor ranking, and integrations. Focused on dependable day-to-day operations.
Edge ML with peer-to-peer coordination and evaluation loops. Covers data ingestion, training, inference, and monitoring on constrained devices.
Dataset and experimentation pipeline for building analytics. Deterministic preprocessing, quality checks, visualization, and neural baselines to compare models fairly.
Engagement flow
A predictable cadence that keeps leadership in the loop and teams unblocked.
Clarify constraints, interfaces, acceptance tests, and success metrics.
Break scope into weekly thin slices with demos and instrumentation.
Ship the slices with tests, tracing/metrics, and rollback plans.
Document, train, and instrument ownership so your team can run it.
Let’s collaborate
Share your context and constraints. You’ll get a scoped plan, milestones, and delivery you can rely on.