I design, build, and scale data-intensive and Generative AI systems end to end — from raw ingestion and modeling to production-grade deployment on cloud platforms.

My work sits at the intersection of Data Science, Machine Learning, Generative AI, and Cloud Architecture, with a strong bias toward systems that are explainable, secure, and usable in real enterprise environments.

• Generative AI & LLM Systems Retrieval-Augmented Generation (RAG), agentic workflows, prompt engineering, evaluation, and governance for real-world use cases.

• Applied Data Science & ML Predictive modeling, NLP, semantic search, and analytics pipelines built on large-scale structured and unstructured data.

• Cloud-Native Engineering Designing and deploying production systems on Azure and AWS, with an emphasis on reliability, scalability, and cost awareness.

• End-to-End Ownership From problem framing and architecture to implementation, deployment, monitoring, and stakeholder communication.

• 10+ years of hands-on experience delivering data, ML, and AI solutions across enterprise and product environments
• Led teams of up to 11 engineers, while also thriving in high-ownership, independent product roles
• Built and deployed production GenAI systems using Python, FastAPI, vector search, cloud AI services, and modern MLOps practices
• Strong background in Azure AI, Azure Data Services, AWS ML services, and cloud-native architectures

• 3+ years mentoring working professionals on applied AI and real-world case studies
• 25+ guest lectures and workshops delivered across universities, global platforms, and professional programs
• Regularly help engineers bridge the gap between theory and production-ready AI systems

Languages & Frameworks Python, SQL, FastAPI, LangChain, Hugging Face, TensorFlow, PyTorch, Spark

GenAI & Search LLMs, RAG architectures, vector databases, semantic search, prompt evaluation

Cloud & Data Platforms Azure (AI, Data, Compute), AWS (ML & data services), Docker, Kubernetes

Engineering Practices API design, MLOps, model evaluation, secure deployments, performance optimization

• Designing privacy-aware, enterprise-grade GenAI platforms
• Improving LLM reliability, grounding, and evaluation in production systems
• Building reusable architectures for copilot-style AI assistants

• Mindfulness at work — sustainable growth, clarity, and long-term career compounding
• Photography — nature, landscapes, and creative composition as a counterbalance to engineering rigor