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Specialized GenAI skills lower generalist competition, but mid-level metro demand keeps competitiveness medium.
Role requires deep LLM and agent engineering expertise, limiting cross-industry transferability.
Explicit years, mandatory GenAI experience, and specific LLM frameworks make filters highly strict.
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Design, develop, and deploy AI-powered applications including AI agents, copilots, autonomous workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, and Google ADK.
Build Retrieval-Augmented Generation (RAG) solutions, semantic search, enterprise knowledge assistants, and integrate AI with business applications, APIs, and enterprise systems.
Package, deploy, and monitor AI applications in cloud environments and contribute reusable AI engineering frameworks and best practices.
3–8 years of overall software engineering experience with at least 2 years in Generative AI, Agentic AI, or LLM-based application development.
Bachelor of Technology degree.
Strong proficiency in Python and experience with REST APIs and frameworks such as FastAPI or Flask.
Experience with cloud deployment (Docker, Azure, AWS, or GCP) and CI/CD fundamentals.
Experienced in building enterprise-grade AI agents, copilots, or automation solutions with strong system design and software engineering fundamentals.
Skilled in using modern AI engineering frameworks and tools for LLM-based applications and RAG systems.
Collaborates effectively across engineering, product, business, and leadership teams with strong communication and stakeholder engagement skills.