Vista previa generada por IA
Senior AI Enablement Engineer – Assistant Manager, Bangalore – Build cutting‑edge AI solutions at State Street.
Join a dynamic team developing Gen AI, RAG and agentic frameworks to power financial services.
Our technology function, Global Technology Services (GTS), is vital to State Street and is the key enabler for our business to deliver data and insights to our clients. We’re driving the company’s digital transformation and expanding business capabilities using industry best practices and advanced technologies such as cloud, artificial intelligence and robotics process automation. We are seeking a motivated and enthusiastic AI Enablement Engineer to join our team and contribute to the development and integration of cutting‑edge AI solutions into our business processes. This is a highly technical and hands‑on role where you will work closely with senior team members to build AI systems leveraging technologies such as Generative AI (Gen AI), Retrieval‑Augmented Generation (RAG), Agentic AI frameworks (LangChain, LangGraph), Graph‑based systems and Large Language Models (LLMs). You will collaborate closely with software engineers and investment servicing business teams to solve complex business challenges in the asset servicing domain.Role Description
- - Develop and maintain AI agent pipelines using LangChain and LangGraph, including ReAct agents, tool‑calling workflows, stateful graph execution and SSE streaming responses.
- - Build and extend RAG pipelines for knowledge‑intensive tasks — document ingestion, chunking, embeddings, vector search and LLM‑based generation.
- - Integrate AI agents with Snowflake, Azure Blob Storage, PostgreSQL and REST APIs to support end‑to‑end data workflows.
- - Design prompt engineering strategies (ReAct, CoT, structured output, few‑shot) for LLMs such as Azure OpenAI.
- - Build and expose agent capabilities via FastAPI and contribute to backend services in Python.
- - Optimize agent pipelines for performance, reliability and scalability in a cloud (Azure) environment.
- - Track, version and manage AI/LLM experiments using MLflow — including logging parameters, metrics, prompts and model artifacts for reproducibility and comparison.
- - Collaborate with cross‑functional teams to identify AI use cases and translate business requirements into agent‑based solutions.
- - Ensure AI solutions comply with responsible AI principles and financial services standards.
Must‑Have Skills
- - 8+ years of professional experience; Bachelor's or Master's in Computer Science, AI/ML or related field.
- - Proficiency in Python with solid software engineering fundamentals.
- - Hands‑on experience with LangChain — chains, agents (ReAct, Tool Calling), LCEL, memory, retrievers and document loaders.
- - Hands‑on experience with LangGraph — StateGraph, stateful multi‑step agent orchestration, conditional edges, checkpointing and human‑in‑the‑loop patterns.
- - Solid understanding of RAG — chunking strategies, embedding models, vector stores and retrieval‑augmented generation.
- - Experience with Azure OpenAI Service and prompt engineering techniques.
- - Familiarity with FastAPI for building and exposing AI agent APIs.
- - Knowledge of vector databases (FAISS, Chroma, Azure AI Search or similar) and semantic search.
- - Experience integrating LLMs with external databases, APIs and storage systems (e.g., Snowflake, Azure Blob Storage).
- - Experience in AI application evaluation — including LLM output quality assessment, RAG evaluation metrics (faithfulness, relevancy, context recall), agent performance benchmarking and use of evaluation frameworks such as RAGAS, LangSmith Evals or Azure AI Evaluation SDK.
- - Strong communication skills with the ability to explain AI concepts to non‑technical stakeholders.
Good to Have
- - Experience with MLflow for experiment tracking, model versioning, prompt management and AI pipeline lifecycle management.
- - Experience with LangSmith or LangFuse for agent tracing and evaluation.
- - Familiarity with Snowflake for data storage, querying and LLM‑integrated workflows.
- - Knowledge of Docker and Kubernetes for deploying AI services.
- - Exposure to other agentic frameworks such as AutoGen, CrewAI or LlamaIndex.
- - Understanding of responsible AI principles and output guardrails.
About State Street
Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.
We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work‑life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.
As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.
Discover more information on jobs at StateStreet.com/careers
Read our CEO Statement