I am a GenAI Engineer with over 3 years of experience transforming advanced AI concepts into production-ready solutions that reduce costs, speed delivery, and boost accuracy. My expertise lies in building safe and observable AI workflows that deliver measurable ROI and earn trust from executives and stakeholders through strong end-user adoption. I excel at collaborating across functions, translating deep technical skills into impactful business outcomes that resonate with clients and leadership.
Throughout my career, I have optimized multi-agent workflows and AI infrastructure to significantly improve latency, throughput, and operational costs at scale. I have developed robust MCP servers with audit trails and orchestration tools that reduce integration lead times and enforce safety guardrails, ensuring zero PII incidents in high-volume environments.
I have launched Agentic RAG systems that enhance accuracy and groundedness while deflecting support tickets, saving operational expenses. My work includes creating LLM-driven pipelines and analytics copilots that reduce forecast errors, automate workflows, and improve data integrity with AI observability tools.
I am skilled in deploying scalable machine learning models using TensorFlow, Docker, and CI/CD pipelines, improving inference latency and experiment cycle times. I have experience building microservices for batch inference and benchmarking, which streamline analyst reviews and improve model deployment.
My projects include developing multi-agent travel planners, autonomous delivery agents, and commerce copilots that leverage hybrid retrieval, policy-aware RAG, and audit trails to deliver high accuracy, cost savings, and user-friendly automation. I am passionate about advancing AI infrastructure and observability to create safe, efficient, and impactful AI systems.
