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Strong employer brand plus metro hiring, but niche LLM specialization limits applicant pool.
Core LLM and agent engineering skills are transferable, but financial-services compliance increases domain specificity.
Explicit 8+ years and many mandatory LLM, LangChain, and Azure skills make filters highly stringent.
Develop and maintain AI agent pipelines using LangChain and LangGraph, including advanced workflows and streaming responses.
Build and extend RAG pipelines for knowledge-intensive tasks like document ingestion, vector search, and LLM-based generation, integrating with databases and cloud storage.
Optimize and expose AI agent capabilities via FastAPI, ensure performance and scalability on Azure cloud, and manage AI/LLM experiments using MLflow.
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 and LangGraph technologies.
Experience with Azure OpenAI Service, prompt engineering, RAG concepts, and integration of LLMs with external databases and APIs (e.g., Snowflake, Azure Blob Storage).
Technical expertise in building and orchestrating complex AI agent workflows within cloud environments, specifically Azure.
Strong background in retrieval-augmented generation and embedding/vector search technologies.
Experience collaborating with cross-functional teams to translate business needs into AI-enabled solutions in asset servicing or financial services contexts.