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Tier-1 brand, mid-level experience band, and metro location increase applicant competition.
Specialized GenAI and agentic engineering skills are transferable across industries but require ML-specific experience.
Explicit 4–7 years plus mandatory LLM, LangChain, RAG, vector DBs, and advanced Python create strict filters.
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Design, develop, and deploy enterprise-grade agentic AI solutions using Java microservices and cloud-native architectures.
Build and optimize autonomous agent applications leveraging LangChain, Crew.ai, SK, Autogen, and implement robust Retrieval Augmented Generation (RAG) systems.
Develop evaluation frameworks, monitoring systems, documentation, and best practices to ensure agent performance, safety, and continuous improvement.
4 to 7 years of work experience in relevant AI and software engineering roles.
Bachelor's or Master's degree in Engineering (BE/BTech, MTech), MCA, or MBA.
Advanced Python programming expertise including async programming and API development.
Experience with vector databases, embedding models, RAG pipeline implementation, prompt engineering, LLM optimization, and modern software development tools (Git, CI/CD, testing).
Experienced in building and deploying autonomous agents with frameworks like LangChain and managing RAG systems.
Strong understanding of LLM APIs, agent safety principles, hallucination resolution, and AI model alignment.
Familiarity with containerization (Docker, Kubernetes), multiple LLM providers, NLP techniques, and agent orchestration workflows.