Head of AI Garage, Wealth & Investment Management Engineering

BNY Mellon • New York, NY

14d

About The Position

Head of AI Garage, Wealth & Investment Management Engineering At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide. Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary. We’re seeking a future team member for the role of Head of AI Garage, Wealth & Investment Management Engineering to join our Wealth & Investment Management Engineering team. This role is located in New York City / Jersey City In this role, you’ll make an impact in the following ways: Build and lead the AI Garage to accelerate, de-risk, and scale GenAI solutions across Wealth Services, Investment Management, and Wealth Management; develop a high-velocity pipeline of use cases aligned to business objectives with a transparent roadmap and OKRs. Establish enterprise-grade data readiness for AI, semantic data foundations, and reusable GenAI platforms, including standards for data quality, lineage, provenance, privacy classifications, and data contracts; implement feature stores, prompt-context catalogs, and instrumentation for usage and drift. Design and operate semantic data and knowledge graph capabilities and vector retrieval infrastructure; implement high-quality RAG pipelines, re-rankers, and citation grounding with robust embedding governance to improve factuality, explainability, and compliance. Deliver end-to-end GenAI applications and LLMOps capabilities (experiment tracking, registries, evaluations, safety filters, prompt/version management); integrate securely with enterprise systems, ensuring scalability, observability, disaster recovery, and alignment with governance, risk, and compliance requirements.

Requirements

• 10+ years in data/AI engineering or applied ML, including 5+ years leading teams delivering production AI systems in regulated environments.
• Proven delivery of enterprise-scale GenAI solutions (e.g., RAG, conversational assistants, document intelligence) with measurable business outcomes; deep experience with LLMs and orchestration (Gemini, Claude, OpenAI; LangChain/LangGraph, DSPy, LlamaIndex), vector embeddings and retrieval (FAISS, Pinecone, Weaviate, pgvector, OpenSearch), knowledge graphs/semantics (GraphDB/Neo4j/Neptune; RDF/OWL, SHACL), and data engineering (Python/Java/Scala; Spark/Flink; SQL/NoSQL; data quality, lineage, metadata).
• Expertise in cloud-native architecture (Kubernetes, microservices, API design, CI/CD, observability, secrets management), privacy/security and controls for financial services (PII handling, encryption, entitlements, model governance), and excellent communication skills to translate complex AI topics into clear business narratives and technical designs.
• Demonstrated people leadership: ability to hire, develop, and lead cross-functional teams (applied scientists, data/graph engineers, LLM engineers, evaluators, product leads) while fostering a culture of craftsmanship, security-by-design, and measurable business impact.

Responsibilities

• Build and lead the AI Garage to accelerate, de-risk, and scale GenAI solutions across Wealth Services, Investment Management, and Wealth Management; develop a high-velocity pipeline of use cases aligned to business objectives with a transparent roadmap and OKRs.
• Establish enterprise-grade data readiness for AI, semantic data foundations, and reusable GenAI platforms, including standards for data quality, lineage, provenance, privacy classifications, and data contracts; implement feature stores, prompt-context catalogs, and instrumentation for usage and drift.
• Design and operate semantic data and knowledge graph capabilities and vector retrieval infrastructure; implement high-quality RAG pipelines, re-rankers, and citation grounding with robust embedding governance to improve factuality, explainability, and compliance.
• Deliver end-to-end GenAI applications and LLMOps capabilities (experiment tracking, registries, evaluations, safety filters, prompt/version management); integrate securely with enterprise systems, ensuring scalability, observability, disaster recovery, and alignment with governance, risk, and compliance requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees