Description

Key Responsibilities

• Agent Logic & Tooling: Develop and maintain the backend "tools" (APIs, scrapers, database connectors) that AI agents use to perform tasks.
• Orchestration Implementation: Use frameworks like LangChain, LangGraph, or CrewAI to implement complex reasoning chains and multi-agent coordination.
• RAG Pipeline Engineering: Build and optimize data ingestion and retrieval systems using Vector Databases , ensuring the agent has the right context at the right time.
• Asynchronous Task Management: Manage long-running AI reasoning cycles using asynchronous Python (FastAPI/Asyncio) and task queues like Celery or Redis.
• API Architecture: Design and implement secure, high-performance REST or GraphQL APIs that serve as the interface between the agentic backend and the frontend.
• Safety & Guardrails: Implement backend-level validation and guardrails to ensure that autonomous agent actions remain within secure and ethical boundaries.

Technical Requirements

• Python Expertise: 8+ years of professional experience with Python , specifically with FastAPI, Pydantic, and Asyncio .
• AI Frameworks: Hands-on experience with LangChain or LlamaIndex .
• Database Management: Proficiency in PostgreSQL and experience with Vector Databases .
• Cloud & DevOps: Experience deploying containerized applications using Docker and Kubernetes on AWS, Azure, or GCP.
• Scalability: Understanding of distributed systems and how to handle the high latency and compute requirements of LLM-based applications.
• Version Control: Mastery of Git and CI/CD best practices.

Preferred Qualifications

• Knowledge of Prompt Engineering from a programmatic perspective (dynamic prompt templating).
• Familiarity with observability tools for AI, such as LangSmith or Arize Phoenix

Key Skills

Education

Any Graduate