• Lead the hands-on architecture, development, and deployment of production-grade AI/ML systems, ensuring scalability, reliability, performance, and cost-efficiency. • Architect traditional ML solutions (e.g., classification, regression, recommendation systems) and advanced GenAI systems including Retrieval-Augmented Generation (RAG) and Agentic AI. • Design and implement cloud-native AI/ML pipelines using cloud platforms. • Evaluate, prototype, and build PoCs regularly to test architectural decisions, validate feasibility, and accelerate solution delivery. • Integrate and deploy multiple LLMs (e.g., from OpenAI, Claude, Gemini, LLaMA, Hugging Face) and vector databases (e.g., Pinecone, Qdrant, pgvector, Milvus, Weaviate). • Create reusable frameworks and solution templates that drive consistency, speed, and quality across AI initiatives. • Ensure all solutions are aligned with responsible AI standards, security best practices, and enterprise governance. • Collaborate with data scientists, ML engineers, software engineers, and enterprise architects to translate business needs into scalable AI solutions. • Provide technical guidance, mentorship, and architectural direction to teams working across the AI/ML lifecycle. • Work in agile teams, contributing hands-on while also shaping backlog priorities and solution design. • Partner cross-functionally to integrate AI into enterprise systems (e.g., Salesforce, Snowflake, Oracle).