Key Responsibilities
Agentic AI Architecture & System Design
- Lead the architecture and development of Agentic AI systems using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, and Semantic Kernel
- Design multi-agent systems with human-in-the-loop decision-making, contextual reasoning, and long-term memory capabilities
- Define scalable, enterprise-grade AI architectures integrating LLMs, APIs, and knowledge systems
LLM Development & Optimization
- Lead experimentation, fine-tuning, and deployment of LLMs such as GPT, Claude, Mistral, LLaMA, and Falcon
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like Pinecone, FAISS, Weaviate, and Milvus
- Implement fine-tuning techniques including LoRA, QLoRA, and PEFT for domain-specific adaptation of models
- Work on prompt engineering, tokenization strategies, and model evaluation metrics
AI Integration & Enterprise Deployment
- Integrate LLMs with enterprise data systems, APIs, and cloud platforms such as AWS, Azure ML, Databricks, and Vertex AI
- Develop and maintain MLOps and LLMOps pipelines for scalable deployment and monitoring
- Ensure robust orchestration of AI workflows across distributed systems
Research, Innovation & Thought Leadership
- Drive research in Generative AI, Agentic AI, and advanced LLM systems
- Evaluate emerging AI frameworks and technologies for enterprise adoption
- Publish internal whitepapers and contribute to AI Center of Excellence (CoE) initiatives
Leadership & Stakeholder Management
- Mentor and guide data scientists and ML engineers in advanced AI system design and prompt engineering
- Collaborate with cross-functional teams including engineering, product, and business stakeholders
- Translate business requirements into AI-driven solutions and strategic initiatives
- Lead proof-of-concept (PoC) development and scale successful solutions into production systems
