Role at a glance
- Job function
-
AI & Data Generative AI Engineering AI Solutions Architecture Large Language Model (LLM) Engineering
- Salary
- Not Disclosed
- Location
- Hyderabad, India
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- 14–18 years’ experience in technology roles, with 5+ years in AI/ML leadership.
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Role Summary
The Assistant Vice President will lead the design and delivery of AI/ML and Generative AI solutions for State Street’s Finance services operations within the Business Solutions team. The role focuses on automation, reporting, data processing, and user experience through LLM applications, agentic workflows, and Python-based solutions.
What You'll Do
- Lead design and delivery of AI/ML and Generative AI solutions for Finance operations.
- Architect and implement agentic workflows for automation of reporting and data processing.
- Build and optimize LLM applications using LangChain, LangGraph, and RAG techniques.
- Develop advanced Python-based pipelines for data wrangling, structured and unstructured data handling, and web scraping.
- Integrate graph databases, embeddings, and JSON-based APIs for scalable solutions.
- Collaborate with stakeholders to translate business requirements into actionable AI-driven solutions.
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View full postingQualifications
Advanced Python; machine learning frameworks including scikit-learn, TensorFlow, and PyTorch; Generative AI, LLMs, RAG, embeddings, and agentic AI; LangChain, LangGraph, workflow automation; structured and unstructured data, JSON, web scraping, graph databases, and relational databases; problem-solving, communication, and stakeholder management skills.
Required
- Advanced Python proficiency
- Machine learning frameworks: scikit-learn, TensorFlow, and PyTorch
- Generative AI, LLMs, RAG, embeddings, and agentic AI
- LangChain, LangGraph, and workflow automation
- Structured and unstructured data, JSON, and web scraping
- Graph databases and relational databases
- Problem-solving, communication, and stakeholder management skills
Preferred
- Experience with cloud platforms (Azure, AWS, GCP) and MLOps practices
- Familiarity with embedding models, vector databases, and semantic search
- Understanding of integration and automation platform (MuleSoft)
Original job description
Content provided by the employer
Original job description
Content provided by the employer
About the Role
We are seeking an Assistant Vice President (AVP) for our Business Solutions team at State Street. This role focuses on leveraging Python (advanced), Machine Learning, and Generative AI technologies to design and implement solutions that enhance operational efficiency, productivity, and user experience in the Finance services domain. The ideal candidate will have expertise in LLMs, RAG, Agentic AI, and automation frameworks, with strong leadership and solution architecture skills.
Key Responsibilities:
- Lead design and delivery of AI/ML and Generative AI solutions for Finance operations.
- Architect and implement agentic workflows for automation of reporting and data processing.
- Build and optimize LLM applications using LangChain, LangGraph, and RAG techniques.
- Develop advanced Python-based pipelines for data wrangling, structured/unstructured data handling, and web scraping.
- Integrate graph databases, embeddings, and JSON-based APIs for scalable solutions.
- Collaborate with stakeholders to translate business requirements into actionable AI-driven solutions.
- Ensure compliance with data governance, security, and model risk management.
Required Skills
- 14–18 years’ experience in technology roles, with 5+ years in AI/ML leadership.
- Strong proficiency in Python (advanced) and ML frameworks (scikit-learn, TensorFlow, PyTorch).
- Expertise in Generative AI, LLMs, RAG, embeddings, and agentic AI.
- Hands-on experience with LangChain, LangGraph, and workflow automation.
- Solid knowledge of structured and unstructured data, JSON, and web scraping.
- Familiarity with graph databases and relational databases.
- Excellent problem-solving, communication, and stakeholder management skills.
Preferred Skills
- Experience with cloud platforms (Azure, AWS, GCP) and MLOps practices.
- Familiarity with embedding models, vector databases, and semantic search.
- Understanding of integration and automation platform (MuleSoft).
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.
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About the company
State Street
Large Enterprise
State Street is a leading financial services and bank holding company, headquartered in Boston, Massachusetts. With a strong focus on investment management and servicing solutions, State Street supports institutional investors worldwide by providing services such as asset management, investment research, and trading. Renowned for its commitment to innovation and sustainability, State Street leverages advanced technology and data analytics to enhance investment strategies and customer experiences. The company prioritizes diversity and inclusion within its workforce, reflecting its dedication to fostering a dynamic and collaborative environment.