Senior Software Engineer

About the job Experience: 5–7 Years Role: Lead Software Engineer Primary Skills: Python, FastAPI, GenAI, Agentic AI, LLM, RAG, AWS, Databricks Required Qualification: B.Tech/B. E or ME/M.Tech (No MCA; M.Sc) - Preferrably CSC, IT, ECE, AI, EEE We are looking for a Senior Software Engineer who is passionate about Generative AI, Agentic systems, and cloud-native API development. In this role, you will be a key contributor in designing, building, and deploying production-grade AI-powered applications and intelligent agent frameworks that transform the way our products and customers interact with data and automation. You will collaborate closely with ML Engineers, Data Scientists, Product Managers, and Cloud Architects to deliver scalable, reliable, and innovative solutions powered by cutting-edge GenAI technologies. What You’ll Do 1. GenAI & Agentic Development Design and develop Agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar. Build and integrate LLM-powered applications using models from OpenAI, Anthropic, Meta (LLaMA), Mistral, or foundation models on cloud platforms. Implement Retrieval-Augmented Generation (RAG) pipelines, vector stores, and semantic search capabilities. Develop multi-agent orchestration systems with defined tool use, memory management, and reasoning loops. Evaluate, fine-tune, and optimize LLM behavior through prompt engineering and chain-of-thought design. 2. API Development & Backend Engineering Build high-performance, scalable RESTful APIs and async microservices using FastAPI and Python. Design clean, modular, and well-documented API contracts following OpenAPI/Swagger standards. Implement authentication, authorization, rate limiting, and security best practices across APIs. Develop event-driven and streaming architectures to support real-time AI inference pipelines. 3. Cloud & Platform Engineering Deploy and manage AI workloads on AWS (SageMaker, Lambda, API Gateway, Bedrock, S3, ECS/EKS). Utilize Databricks for large-scale data processing, MLflow model tracking, and AI/ML pipeline orchestration. Build CI/CD pipelines and infrastructure-as-code (IaC) using tools like Terraform, AWS CDK, or similar. Ensure scalability, observability, and reliability through cloud-native monitoring and logging practices. 4. Engineering Excellence Write clean, testable, production-ready Python code following software engineering best practices. Conduct code reviews and mentor junior engineers on GenAI patterns and best practices. Contribute to architectural decisions, technical roadmaps, and engineering documentation. Stay current with the rapidly evolving GenAI ecosystem and champion the adoption of emerging technologies. Interested candidates can share their updated resumes along with their current CTC, expected CTC, Offer in hand (If Any), notice period, Qualification and current location.