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Tier-1 Bangalore role but niche LLM/agent skills and seniority moderate applicant competition.
Agentic LLM and engineering skills are transferable, but asset-servicing domain and compliance increase specialization.
Explicit 8+ years, mandatory LLM/agent technologies and finance compliance create strict screening filters.
Develop and maintain AI agent pipelines using LangChain and LangGraph for complex AI workflows in a financial services environment.
Build and integrate Retrieval-Augmented Generation (RAG) pipelines and AI agent systems with cloud services (Azure), databases (Snowflake, PostgreSQL), and REST APIs to support asset servicing business use cases.
Optimize AI solutions for performance, scalability, and compliance with responsible AI principles while collaborating with cross-functional teams to translate business needs into AI implementations.
8+ years of professional experience.
Bachelor's or Master's degree in Computer Science, AI/ML, or related field.
Proficiency in Python and hands-on experience with LangChain, LangGraph, RAG pipelines, Azure OpenAI services, and FastAPI.
Experience integrating AI with cloud services (Azure), data storage solutions (Snowflake, Azure Blob Storage), and familiarity with vector databases for semantic search.
Strong expertise in advanced AI frameworks, specifically LangChain and LangGraph, with a focus on agent orchestration and retrieval-based generation.
Experience working in financial services technology with ability to implement AI-driven business solutions in asset servicing domain.
Comfortable handling end-to-end AI system lifecycle including experiment tracking, performance benchmarking, and compliance with responsible AI standards in cloud environments.