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Tier-1 brand, metro location, and mid-level experience range increase competition despite niche GenAI focus.
Specialized GenAI, agent, LLM and RAG experience is transferable but favors candidates with direct domain experience.
Explicit 4–7 years plus mandatory LangChain, RAG, vector DB and advanced Python skills makes screening highly strict.
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Design and implement autonomous AI agents using frameworks like LangChain, Crew.ai, SK, Autogen, focusing on enhancing agent capabilities and safety.
Develop and optimize Retrieval Augmented Generation (RAG) systems for better information retrieval and response generation in AI applications.
Create evaluation frameworks, implement monitoring/logging for agent behavior, and contribute to documentation and best practices for agent development.
4 to 7 years of relevant experience in AI/automation engineering.
Mandatory skills include advanced Python (async programming, API development), experience with vector databases, embedding models, and RAG pipelines.
Proficiency in modern software development practices (Git, CI/CD, testing) and knowledge of large language models (LLMs) APIs and prompt engineering.
Educational qualification: Bachelor of Engineering (BE/BTech), Master of Business Administration (MBA), or equivalent (MCA/MTech).
Experienced in building and deploying autonomous AI agents with strong hands-on expertise in frameworks like LangChain and RAG systems.
Demonstrates depth in AI safety principles, hallucination resolution, and agent behavior monitoring.
Familiarity with multiple LLM providers, NLP techniques, container technologies, and agent orchestration preferred, indicating capability to work on complex AI deployments.