Fullstack · AI Engineering
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I ship fullstack platforms, streaming/data backbones, and AI/LLM features with Spring Boot, Angular, Kafka, Python/PyTorch, and Spring AI. Four years delivering scoped work with tests, observability, and handoff.
A strong first message saves time and leads to a better plan.
Goal & “done” definition
What should improve: latency, accuracy, reliability, cost, or delivery date.
Current stack & environment
Backend/framework, data stores/streaming, ML tooling, infra (Docker/K8s), monitoring.
Constraints & risks
Deadlines, data availability, compliance/security, budget, support expectations.
Engagement type
One-off audit, delivery sprint, or part-time ongoing support.
Links (optional, helpful)
Repo, docs, sample data, dashboards, logs—anything that reduces guesswork.
Research
I’m pursuing a PhD on decentralized peer-to-peer learning for time-series modeling, with a focus on practical deployment constraints (edge devices, privacy, reproducibility). In client work, this translates into rigorous evaluation, reproducible pipelines, and pragmatic engineering decisions.
How it helps your project