Description

You build and operationalize complex agentic and Retrieval-Augmented Generation (RAG) systems at scale.

This role is on-site.

Responsibilities

• Design multi-agent architectures, defining roles, memory strategy, and supervisor policies for safe collaboration.
• Build high-quality RAG pipelines, implementing ingestion, chunking, embeddings, indexing, retrieval, and evaluation checks.
• Productionize ML workflows on AWS, leveraging services like Bedrock, Lambda, S3, DynamoDB, and Step Functions.
• Automate ML/LLMOps lifecycle using CI/CD (GitOps), containerization (Docker/Kubernetes), and infra-as-code.
• Instrument telemetry, build monitoring dashboards, and implement continuous offline and online evaluation loops.

Required Skills

• 10+ years of proven experience building agentic systems and RAG pipelines in production.
• Strong background in cloud environments for AI/ML workloads, including familiarity with Bedrock or equivalent LLM platforms.
• Solid skills in CI/CD practices, Git, Docker, and Kubernetes, coupled with infra-as-code fundamentals.
• Experience with key AWS services: S3, DynamoDB, API Gateway, OpenSearch, and Lambda.
• Knowledge of data governance and model accountability within the MLOps/LLMOps framework.
• Ability to document designs, SLIs/SLOs, and communicate technical insights to varied audiences.
• Experience implementing reliability patterns like caching, rate-limiting, and idempotency.

Key Skills

Education

Any Graduate