AI Engineer · DevOps & Cloud Engineer · Java Full-Stack Developer
Building reliable agentic AI, cloud-native platforms, and production-grade backend systems.
I turn ambitious ideas into secure, observable, and maintainable software.
Agentic AI → tool-using agents, RAG pipelines, and multi-agent workflows
Cloud platforms → containerized services, CI/CD, infrastructure as code, and observability
Backend systems → secure Java/Spring Boot APIs, data pipelines, and scalable services
Automation → reliable workflows that remove repetitive operational work
- Currently building production-minded AI workflows with LangGraph, LangChain, RAG, and MCP.
- Exploring safer multi-agent orchestration, evaluation, and human-in-the-loop patterns.
- Comfortable moving from architecture to implementation, deployment, monitoring, and iteration.
- I care about deterministic behavior, security boundaries, clean APIs, and useful developer experience.
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A full-stack financial reconciliation platform with a deterministic Java engine, auditable exception workflows, streaming CSV ingestion, role-based access, and optional Gemini-powered advisory analysis.
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A growing collection of practical machine-learning and deep-learning implementations for learning, experimentation, and reference.
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My rule for AI systems: use deterministic software for decisions, and generative AI for assistance where its output can be reviewed.
| AI & orchestration | Backend & data | Cloud & delivery | Frontend |
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| LangGraph · LangChain · RAG · MCP · Gemini · OpenAI | Java · Spring Boot · Python · FastAPI · PostgreSQL · MySQL · MongoDB | AWS · Azure · GCP · Docker · Kubernetes · Terraform · GitHub Actions · Jenkins · Linux | React · TypeScript · JavaScript · Tailwind CSS · HTML · CSS |