Key Responsibilities AI Solutioning & Architecture Lead the design and implementation of end-to-end AI solutions ensuring scalability, robustness, and efficiency aligned with business needs. Architect RAG pipelines using frameworks like LangChain, LlamaIndex, or custom-built stacks. Design Agentic AI architectures, including task-based agents, stateful memory, planning-execution workflows, and tool augmentation. Data Strategy & AI Model Development Define and execute data strategies for collection, cleaning, transformation, and integration. Fine-tuning & Prompt Engineering: Fine-tuning pre-trained models (e.g., GPT, BERT, etc.) and optimize prompt engineering techniques to drive high-quality, actionable outputs for diverse business use cases. Perform embeddings generation, evaluation of outputs, and incorporate human/automated feedback loops. Apply advanced NLP techniques such as tokenization, prompt engineering, and query optimization. Machine Learning & Deep Learning Models: Build, train, and deploy machine learning models, including deep learning models, for complex AI applications across various domains. AI Guardrails & Safety Build and enforce guardrails for model safety and compliance, including prompt validation, output moderation, and access controls. Ensure solutions meet data governance, compliance, and security standards. Deployment & Cloud-Native Enablement Collaborate with teams to deploy solutions in AWS cloud-native environments (Bedrock, Lambda, ECS, SageMaker, CDK). Oversee CI/CD pipelines, API integrations, and scalable production deployments. Lead LLM provisioning from AWS, balancing performance and cost-effectiveness. Deployment & Evaluation: Oversee the deployment of AI models, ensuring smooth integration with production systems, and perform rigorous evaluation of LLMs for accuracy, efficiency, and scalability. Observability & post-deployment Contribute to system observability. Support post-deployment monitoring, optimization, and retraining cycles for LLM-driven systems. Technologies & Frameworks LLM: Expertise in AWS Bedrock RAG: LangChain, LlamaIndex, CrewAI, VectorDB Programming: Python Cloud Platforms: AWS (Bedrock, SageMaker, Lambda, CDK) Data & Databases: SQL, NoSQL, Data Lakes, Data Warehouses. Orchestration & Deployment: CI/CD pipelines, containerized microservices, Kubernetes. Required Skills & Qualifications Proven production experience with RAG pipelines (LangChain, LlamaIndex, or custom stacks). Strong understanding of Ag.