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Tier-1 brand and metro location increase competition; specialized LLM agent skills however reduce candidate density.
Core LLM, AI-agent, and cloud engineering skills are highly transferable across industries.
Extensive mandatory LLM, AI-agent, cloud, and DevOps tech stack requirements enforce strict candidate filters.
Design and implement LLM-driven AI agent services focusing on code generation, documentation, testing, and observability on AWS.
Develop orchestration and communication layers between AI agents and integrate them with toolchains like Jira, GitHub, and Terraform.
Provide technical leadership and drive adoption of AI-assisted engineering practices to improve code quality, speed, and operational outcomes across teams.
Degree in Computer Science or Machine Learning related field (BE/B.Tech, ME/MS, or PhD).
Strong hands-on experience with Python, Pydantic, FastAPI, LangGraph, and vector databases for RAG-based AI agent solutions on AWS (EKS, Lambda, S3, Terraform).
Experience with LLM integration, prompt engineering, AI Agent frameworks such as LangChain, LangGraph, Autogen, MCPs, A2A.
Solid understanding of Cloud platforms (AWS/Azure/GCP) and DevOps tools (CI/CD, Terraform, Kubernetes, Docker, APIs).
Experienced in building and deploying large-scale AI agent and orchestration solutions heavily integrating with multiple AI frameworks and cloud services.
Skilled in bridging data science with software engineering and DevOps to deliver scalable AI-driven software products.
Capable leader who can mentor junior engineers and influence AI-assisted engineering practices adoption across multiple teams.