Description

You will own the end-to-end lifecycle of agentic and Retrieval Augmented Generation (RAG) systems in production.

Responsibilities

• Design multi-agent architectures, defining roles, memory strategy, and supervisor policies for safe collaboration.
• Build and evaluate high-quality RAG pipelines, implementing ingestion, chunking, embeddings, indexing, and guardrails.
• Productionize ML/LLM workloads on AWS using services like Bedrock, Lambda, S3, and DynamoDB.
• Automate MLOps/LLMOps pipelines, managing CI/CD, containerization, and infrastructure-as-code.
• Instrument telemetry for observability, build performance dashboards, and manage continuous evaluation loops.

Required Skills

• Proven experience building agentic systems and RAG pipelines in production.
• 5+ years of relevant professional experience.
• Bachelor's degree in Computer Science, Data Science, Engineering, or equivalent experience.
• Strong background in AWS, including familiarity with AI/ML workloads.
• Solid skills in CI/CD, Docker, Kubernetes, and infrastructure-as-code.
• Experience with Git and containerization practices.
• Familiarity with LangGraph.
• Knowledge of data governance and model accountability in MLOps.
• Excellent communication and problem-solving abilities.

Key Skills

Education

Any Graduate