Description

You will design, build, and deploy scalable agentic AI systems and backend infrastructure. You own the full lifecycle from agent architecture and RAG pipelines to production deployment and observability.

Responsibilities

• Develop agentic AI systems in Python using LangGraph and LangChain, implementing supervisor/sub-agent architectures, tool routing, and human-in-the-loop workflows.
• Build and optimize end-to-end RAG pipelines, covering document ingestion, chunking, embedding, hybrid retrieval, re-ranking, and access-control filtering.
• Create scalable backend services and microservices in FastAPI, ensuring secure API design, request validation, and rate limiting.
• Design and manage data stores including PostgreSQL, MongoDB, and Redis, handling schema migrations, connection pooling, and query performance.
• Implement LLM gateway infrastructure, observability practices, and prompt engineering standards to mitigate hallucination and injection risks.

Required Skills

• 7+ years of experience with Python, FastAPI, and RESTful API design.
• Strong proficiency with LangGraph, LangChain, and agentic AI patterns.
• Experience with PostgreSQL, Docker, and Kubernetes deployment strategies.
• Knowledge of LLM integration, RAG pipelines, and vector databases.
• Familiarity with Okta, SSO, and enterprise identity management.
• Understanding of MCP servers/clients or A2A-based agent interoperability.
• Experience with evaluation frameworks like LangSmith or Ragas.
• Familiarity with workflow orchestration tools such as Prefect, Temporal, Airflow, or Dagster.

Key Skills

Education

Not specified