GenAI Engg.- MLSE/Lead

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We are looking for a Senior Data 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

• ground.
• Proficiency in Python and SQL with hands-on work in PySpark, Airflow, or Glue.
• 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).
• 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.

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We are looking for a Senior Data 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

• ground.
• Proficiency in Python and SQL with hands-on work in PySpark, Airflow, or Glue.
• 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).
• 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.