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Tier-1 brand increases competition despite niche senior LLM specialization.
Specialized LLM and enterprise agentic AI skills transfer across industries, but banking governance increases domain preference.
Explicit 8-11 years, mandatory GenAI/LLM, LangChain and vector DB requirements raise shortlisting strictness.
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Design, build, and scale enterprise-grade Generative AI and Agentic AI applications leveraging LLMs like GPT, Claude, or Gemini and frameworks like LangChain and CrewAI.
Develop and optimize Retrieval-Augmented Generation (RAG) architectures integrating vector databases and enterprise knowledge systems to improve AI content relevance and grounding.
Build scalable backend services and cloud-native microservices using Java and/or Python; implement AI governance, monitor performance, and optimize inference costs in production environments.
8-11 years of software engineering experience.
2+ years of hands-on experience building Generative AI or LLM-powered applications.
Proficiency with GPT, Claude, Gemini or similar LLMs, AI Agent frameworks (e.g., LangChain, LangGraph, CrewAI), RAG, vector databases, prompt engineering, and programming in Java and/or Python.
Experience building REST APIs and microservices; familiarity with cloud platforms and modern software development practices.
Engineer experienced in deploying production-grade AI systems focusing on Generative AI and Agentic AI with proficiency in AI frameworks and modern cloud-native architectures.
Strong software engineering background with ability to deliver scalable back-end services integrated with AI capabilities.
Operationally minded professional capable of AI operations, governance, cost optimization, and driving adoption of emerging AI technologies in large-scale user environments.