Lead Data Engineer

About the Role: We are looking for a Software Engineer who combines deep data engineering expertise with hands-on experience in Generative AI and Agentic AI system development on AWS Cloud. This role is ideal for someone who can design, build, and deploy production-grade GenAI workflows integrating LLMs, vector databases, and orchestration frameworks—with the same rigor as a traditional data system. Key Responsibilities: • Design and maintain data pipelines and AI data infrastructure on AWS (Glue, Lambda, S3, Redshift, Step Functions, Athena, etc.). • Develop and deploy LLM-based applications and Agentic AI workflows using frameworks like LangChain, LlamaIndex, or AutoGen. • Build RAG (Retrieval-Augmented Generation) pipelines using AWS services (S3 + Bedrock + SageMaker + OpenSearch/Vector DB). • Implement agentic reasoning, tool calling, and orchestration for multi-agent workflows. • Containerize and deploy AI services using Docker, ECS, or EKS, ensuring scalability, cost-efficiency, and observability. • Integrate AWS Bedrock, SageMaker, or OpenAI APIs with internal data systems and applications. • Set up monitoring, tracing, and model observability using AWS CloudWatch, X-Ray, or third-party LLMOps tools. • Collaborate with ML engineers, data scientists, and architects to take GenAI prototypes to production-ready deployments. Required Skills & Experience: • 6–10 years of total experience in Data Engineering with strong AWS background. • Proficiency in Pyspark with hands-on production grade experience. • Hands-on experience with GenAI solutions in real-world environments (not just demos or PoCs). • Working knowledge of Agentic AI frameworks (LangChain, LlamaIndex, AutoGen, or similar). • Good hands-on experience in Python • Cloud experience is must have, AWS is preferred. • Experience with RAG architecture, vector databases (OpenSearch, Pinecone, FAISS, Chroma, or Milvus), and embedding models. • Understanding of LLMOps, prompt lifecycle management, and performance monitoring. • Practical experience deploying workloads on AWS ECS/EKS, setting up CI/CD pipelines, and managing runtime performance. • Familiarity with IAM, VPC, Secrets Manager, and security best practices in cloud environments. Nice to Have: • Experience with AWS Bedrock for model hosting or SageMaker for fine-tuning and evaluation. • Exposure to multi-agent architectures and autonomous task orchestration. • Contributions to open-source GenAI projects or internal AI platform initiatives. For Quick Response- Interested Candidates can directly share their resume along with the details like Notice Period, Current CTC and Expected CTC at