Senior Backend Engineer (AI & Agentic Systems)
Experience: 8+ YearsRole Overview

We are looking for a highly experienced Senior Software Engineer to lead the design and implementation of scalable backend systems that power next-generation Agentic AI applications. In this role, you will bridge the gap between robust distributed architecture and cutting-edge generative AI. You will move beyond simple API wrappers to build production-ready multi-agent systems, RAG pipelines, and intelligent automation workflows.

This is a hands-on technical role ideal for a seasoned engineer who has mastered Python backend development and has successfully pivoted into the Agentic AI space using tools like LangChain, LangGraph, and CrewAI.

Key Responsibilities

System Architecture: Design and build high-performance, distributed backend systems using Python (FastAPI/Django) that support real-time AI processing and high concurrency. AI Orchestration: Architect and deploy multi-agent systems using LangChain, LangGraph, and CrewAI, enabling autonomous workflows that solve complex business problems. RAG Implementation: Develop advanced Retrieval-Augmented Generation (RAG) pipelines utilizing vector databases and LLM APIs (OpenAI, Vertex AI) to improve model accuracy and context. Infrastructure & DevOps: Manage containerized deployments using Docker and Kubernetes across AWS or GCP environments. Ensure CI/CD pipelines are robust and scalable. Database Design: Optimize data storage strategies using PostgreSQL and MongoDB, ensuring data integrity for both transactional records and vector embeddings. Integration: Implement Model Context Protocol (MCP) adaptors and integrate various third-party APIs (e.g., Google ADK) into a unified backend ecosystem. Mentorship: Act as a technical lead, mentoring junior developers on backend best practices, system design patterns, and AI integration techniques.

Required Skills & Qualifications

Experience: 8+ years of professional software engineering experience with a focus on backend systems. Core Languages: Expert-level proficiency in Python. Frameworks: Deep experience with FastAPI and Django for building RESTful APIs. AI & GenAI Stack: Proven experience building Agentic AI systems (not just chatbots). Hands-on expertise with LangChain, LangGraph, and CrewAI. Experience integrating OpenAI APIs and Google Vertex AI in production. Solid understanding of RAG architectures and vector search. Cloud & DevOps: Strong command of AWS or GCP, along with Docker, Kubernetes, and Linux server administration. Databases: Proficiency in SQL (PostgreSQL) and NoSQL (MongoDB) environments. Problem Solving: A track record of solving real-world problems (e.g., automated reporting, document analysis) by combining engineering logic with AI capabilities.

Preferred Qualifications (Bonus)

Frontend: Proficiency in ReactJS to build or assist with dashboard interfaces for AI agents. Tools: Experience with Model Context Protocol (MCP) adaptors and Google ADK. Project Experience: Prior work building automated query generators (e.g., Splunk/SQL) or AI-driven document processing systems.Tech Stack Summary Backend: Python, FastAPI, Django AI/ML: LangChain, LangGraph, CrewAI, OpenAI, Vertex AI Database: PostgreSQL, MongoDB, Vector DBs Infrastructure: AWS/GCP, Kubernetes, Docker Frontend: ReactJS