AI Engineer, Assistant Vice President

State Street is a global organization providing services to institutional investors to help manage risk and drive performance. The role operates within an enterprise-scale setting blending start-up energy with established infrastructure.

• Strong programming skills in Python.
• Experience building production-grade AI/ML systems.
• Experience with microservices and API-based architectures.
• Hands-on experience with LLMs, prompt engineering, RAG pipelines, embeddings, vector search, and retrieval systems.
• Experience building agent-based AI workflows.
• Experience with AI platforms such as Databricks, Azure AI, or AWS AI services.
• Knowledge of MLOps / LLMOps practices, including deployment and monitoring.
• Ability to optimize systems for latency, scale, and cost.
• Understanding of data engineering concepts and distributed systems.
• Ability to integrate AI systems with enterprise data platforms and APIs.
• Experience working in regulated or enterprise environments.
• Ability to translate business needs into practical AI solutions.
• Strong collaboration and communication skills.
• B.S. degree in Computer Science, Engineering, Mathematics, Physics, or other technical field required; M.S. degree strongly preferred.
• Minimum 8-10 years of relevant experience including 3+ years in AI/ML/Generative AI.

Nice to have:

• Experience in financial services or capital markets.
• Familiarity with Databricks or Snowflake.
• Experience building AI copilots, document intelligence solutions.
• Exposure to AI governance or model risk frameworks.

AI Engineering & Development:

• Design, build, and deploy production AI and ML solutions.
• Develop LLM-based applications including prompt engineering and inference workflows.
• Build RAG pipelines using structured and unstructured enterprise data.
• Implement embeddings, semantic search, and vector-based retrieval.
• Develop agent-based and multi-step AI workflows.

Production Delivery & Operations:

• Build and maintain AI services and APIs for enterprise use.
• Implement MLOps / LLMOps practices including model deployment, versioning, monitoring, evaluation, logging, and rollback strategies.
• Optimize systems for performance, scalability, and cost efficiency.
• Integrate AI solutions with Databricks, Snowflake, and cloud platforms (Azure / AWS).

Data & Integration:

• Ensure data readiness for AI use cases including quality, access, and transformation.
• Integrate AI solutions with enterprise data platforms and APIs.
• Partner with data teams to support AI-driven pipelines and workflows.

Governance & Risk:

• Implement controls for data privacy, security, and model reliability.
• Apply techniques for validation, explainability, and hallucination mitigation.
• Align with enterprise standards for compliance and auditability.

Use Case Delivery:

• Translate business problems into deployable AI solutions.
• Deliver use cases such as intelligent document processing, AI copilots for operations and analytics, and data-driven insights for markets and corporate functions.

The role requires a minimum of three days per week working from the office.