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Mid-level experience range plus recognizable employer and popular AI role, but specialized GenAI skills moderate competition.
Role demands niche GenAI/LLM agent and RAG experience, making cross-industry transferability limited.
Explicit years, mandatory Generative AI hands-on experience, and specific LLM/tooling requirements create strict filters.
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Design, develop, and deploy AI-powered applications including AI agents, copilots, and autonomous workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, and Google ADK.
Build Retrieval-Augmented Generation (RAG) solutions, semantic search, and enterprise knowledge assistants integrated with business applications, APIs, and enterprise systems.
Package, deploy, monitor AI applications in cloud environments (Azure, AWS, GCP) and contribute reusable AI engineering frameworks and best practices.
3 to 8 years of overall software engineering experience with minimum 2 years hands-on in Generative AI, Agentic AI, or LLM-based application development.
Strong proficiency in Python, REST API integrations, and frameworks like FastAPI or Flask.
Experience with agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, Google ADK), RAG, vector databases, and cloud deployment using Docker and CI/CD tools.
Bachelor of Technology degree.
Hands-on AI Engineer with solid software engineering fundamentals and at least 2 years building AI agents, copilots, and RAG-based solutions in enterprise or production environments.
Strong collaborator who can effectively engage and influence cross-functional teams including engineering, product, business, and leadership.
Confident communicator able to conduct demos, presentations, workshops, and explain complex AI concepts to diverse audiences.