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Tier-1 brand and Bangalore increase competition, but niche LLM/agent seniority reduces applicant density.
Specialized LLM/agent engineering skills are transferable across industries but require strong ML expertise.
Explicit 8+ years and many mandatory LLM/agent and integration skills create strict filtering.
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Develop and maintain advanced AI agent pipelines using LangChain and LangGraph, including integrating with cloud infrastructure and backend services.
Design and optimize Retrieval-Augmented Generation (RAG) pipelines for knowledge-intensive tasks and integrate AI solutions with databases and APIs such as Snowflake and Azure Blob Storage.
Collaborate with cross-functional teams to translate business needs into AI-based solutions while ensuring compliance with responsible AI principles and financial services standards.
8+ years of professional experience in AI/ML or related fields.
Bachelor's or Master's degree in Computer Science, AI/ML, or a related technical field.
Proficiency in Python and hands-on experience with LangChain, LangGraph, RAG, and Azure OpenAI Service including prompt engineering.
Experience integrating AI/LLM systems with external databases (Snowflake, Azure Blob Storage), REST APIs, and using FastAPI for AI service deployment.
Senior technical contributor with deep expertise in building and orchestrating stateful AI agent workflows using LangChain and LangGraph frameworks.
Experienced in cloud-native AI service development, particularly on Azure, and managing AI/ML experiments with tools like MLflow.
Skilled in translating complex business challenges in financial services into scalable, compliant AI solutions with strong evaluation of AI output quality and performance.