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Tier-1 brand and Bangalore location plus mid-level experience, but niche GenAI reduces broad applicant competition.
Medium: GenAI skills transferable across sectors, but agentic AI and RAG expertise favors AI-focused roles.
High: explicit 4–7 years, mandatory GenAI experience and specific LLM,RAG, Python and cloud deployment requirements.
Design, develop, and deploy production-ready Generative AI (GenAI) solutions including Retrieval-Augmented Generation (RAG) systems and multi-agent orchestration using state-of-the-art frameworks like LangChain, AutoGen, and LangGraph.
Integrate graph-based memory and tool-using agents to enhance Large Language Model (LLM) capabilities for enterprise use cases.
Ensure system scalability, reliability, and maintainability through strong software engineering practices and cross-functional collaboration.
2–5 years of total experience in AI/ML or software engineering, with minimum 2 years hands-on experience in building and deploying GenAI systems.
Proficiency in Python programming and familiarity with cloud-native deployments (APIs, containers, microservices).
Experience working with Large Language Models such as GPT-4, Claude 2, or Gemini, and knowledge of RAG and multi-agent frameworks like LangChain, LangGraph, or AutoGen.
Education: Bachelor of Engineering, Master of Engineering, MBA, or equivalent.
Experienced AI Engineer with deep expertise specifically in Generative AI and agentic AI technologies, capable of handling complex LLM integrations and multi-agent frameworks.
Strong software engineering background with ability to deliver scalable, maintainable AI systems in production environments.
Familiar with advanced AI tooling ecosystems and cloud-based LLM services, and able to translate business needs into practical AI solutions.