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Tier-1 brand, metro location, mid-level role, and hot GenAI skills create high applicant competition.
Specialized GenAI and agent engineering skills limit easy industry transfer, so sensitivity is medium.
Explicit years, mandatory GenAI experience, and required LLM/framework skills imply high shortlisting strictness.
Design, develop, and deploy GenAI solutions focusing on real-world production environments using Retrieval-Augmented Generation (RAG) and multi-agent frameworks.
Build and scale enterprise-grade RAG systems incorporating graph-based memory and tool-using agents to enhance LLM capabilities.
Collaborate with cross-functional teams ensuring system scalability, reliability, and maintainability via strong software engineering practices.
2 to 5 years total experience in AI/ML or software engineering with at least 2 years hands-on in Generative AI system development and deployment.
Proficiency in Python and experience with LLMs such as GPT-4, Claude 2/Gemini, including strong knowledge of RAG systems and multi-agent frameworks (e.g., LangChain, AutoGen, LangGraph).
Bachelor’s or Master’s degree in Engineering, MCA, or MBA.
Experience with cloud-native deployments including APIs, containers, and microservices.
Experienced AI/ML professional specializing in Generative AI and agentic AI frameworks with practical production deployment expertise.
Strong software engineering background with ability to build scalable, maintainable AI-driven systems integrating advanced LLM toolkits and graph-based memory.
Comfortable collaborating in cross-functional teams to translate business requirements into robust AI solutions leveraging state-of-the-art GenAI technologies.