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Specialized LLM skillset reduces applicant pool despite metro location and mid-level experience.
Highly specialized LLM, agent, and RAG expertise limits cross-industry portability.
Explicit 3–8 years plus mandatory Generative AI and specific LLM stack requirements tighten screening.
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Design, develop, and deploy AI agents, copilots, and autonomous workflows using LangChain, LangGraph, CrewAI, AutoGen, Google ADK, and related frameworks.
Develop Retrieval-Augmented Generation (RAG) solutions, semantic search systems, and enterprise knowledge assistants integrated with business APIs and databases.
Package, deploy, and monitor AI applications in cloud environments and contribute AI engineering best practices and reusable frameworks.
3–8 years overall software engineering experience with at least 2 years hands-on in Generative AI, Agentic AI, or LLM-based application development.
Strong proficiency in Python programming, REST APIs, and frameworks like FastAPI or Flask.
Experience with AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Google ADK and with RAG and vector databases like Pinecone or Chroma.
Bachelor of Technology degree.
Experienced in end-to-end AI agent development and comfortable integrating AI solutions into complex enterprise environments.
Skilled in cloud deployments with containerization and CI/CD fundamentals, capable of delivering production-ready AI applications.
Able to collaborate across multidisciplinary teams including engineering, product, and business stakeholders and communicate technical concepts clearly.