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Tier-1 brand and metro location increase competition, but niche LLM/agent expertise limits applicant density.
Requires specialized LLM, agent frameworks, and vector DB expertise, limiting cross-industry interchangeability.
Mandatory 8-11 years, 2+ years GenAI, and specific LLM/agent and tech stack requirements create strict filters.
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Design, build, and deploy enterprise-grade Generative AI and Agentic AI applications using LLMs like GPT, Claude, or Gemini, including multi-agent systems to automate workflows and customer journeys.
Develop and optimize Retrieval-Augmented Generation (RAG) architectures integrating vector databases and knowledge repositories to enhance AI content relevance and accuracy.
Build scalable backend microservices and APIs with Java/Python, collaborate cross-functionally to deliver production-ready AI-powered digital experiences, and manage AI governance, model performance, and operational costs.
8-11 years of software engineering experience.
At least 2 years of hands-on experience building Generative AI or Large Language Model-powered applications.
Strong programming skills in Java and/or Python; experience building REST APIs and microservices.
Experience with GPT, Claude, Gemini or similar LLMs, Agent frameworks such as LangChain, LangGraph, or CrewAI, Retrieval-Augmented Generation (RAG), and vector databases.
Has deep expertise in designing and operating production-grade Generative AI and Agentic AI systems at scale, emphasizing operational efficiency and AI governance.
Experienced working with cloud-native microservice architectures and integrating cutting-edge AI models into customer-facing applications.
Familiar with advanced prompt engineering, AI evaluation frameworks, and optimizing inference costs in a collaborative product development environment.