• Engineer Generative AI Data Systems • Design and maintain data pipelines for training, fine-tuning, and retrieval-augmented generation (RAG) use cases. • Build ingestion frameworks using AWS Glue, Lambda, Kinesis, and Step Functions to support large-scale AI workloads. • Develop embedding pipelines, feature stores, and vector database integrations (Pinecone, FAISS, Chroma, Amazon OpenSearch) to power semantic retrieval. • Transform unstructured data–documents, text, images, logs–into AI-ready assets for LLM applications. • Integrate & Orchestrate LLM Architectures • Build end-to-end GenAI pipelines connecting enterprise data with LLMs including Anthropic Claude, Amazon Titan, OpenAI GPT, and Llama 3. • Use LangChain, LlamaIndex, and Bedrock Agents to deliver context-rich RAG, prompt-chaining, and conversational intelligence. • Develop LLM-powered APIs enabling natural language querying, summarization, search, and generative workflows. • Optimize prompts, context windows, model evaluation, and response quality. • Scale AI Infrastructure & MLOps • Deploy, monitor, and optimize LLM workflows on AWS Bedrock and other cloud AI platforms. • Implement CI/CD pipelines for GenAI systems using Airflow, Prefect, GitHub Actions, or AWS CodePipeline. • Establish data and model observability frameworks to track drift, accuracy, latency, and performance. • Partner with Data Science and MLOps teams to streamline fine-tuning, deployment, and scalable model operations. • Champion Governance, Security & Responsible AI • Implement data lineage, access controls, encryption, and governance for AI datasets. • Enforce Responsible AI practices, ensuring transparency, risk mitigation, and ethical use of LLMs. • Maintain prompt logs, telemetry, and audit documentation supporting SOC2, GDPR, and CCPA compliance.