I’m a Senior Software Engineer with 4+ years of experience building scalable Python backends, distributed systems, and production-grade AI platforms. My work sits at the intersection of backend engineering and applied AI — building reliable systems that integrate LLMs, agents, and intelligent automation into real-world enterprise workflows. Currently, I work on production Agentic AI systems, including supervisor-agent architectures, RAG pipelines, hybrid semantic search, and LLM-powered automation. I focus not just on making AI systems work, but on making them scalable, observable, maintainable, and production-ready. My core areas of expertise include: • Agentic AI & multi-agent workflows with LangGraph • Production RAG, hybrid vector + keyword retrieval, and enterprise knowledge enrichment • Azure OpenAI & Azure AI Search • Backend engineering with Python, FastAPI & AsyncIO • Distributed APIs, microservices & asynchronous processing • PostgreSQL, Couchbase & Redis • AWS & Azure cloud platforms • Docker, Kubernetes & CI/CD • LLM prompt governance, versioning, traceability & monitoring with LangSmith At Siemens, I built and optimized backend platforms and microservices supporting data systems across 10+ power plants, including ETL orchestration with Airflow and event-driven AWS architectures. I also owned the development of 60+ production REST APIs using FastAPI and async I/O. I enjoy solving difficult engineering problems where scale, reliability, system design, and AI come together — especially turning experimental LLM capabilities into systems that can actually survive production. I’m particularly interested in building scalable AI infrastructure, agentic systems, and high-performance backend architectures. Open to opportunities in AI Engineering, Agentic AI, AI Platform Engineering, and backend systems at scale.