Senior Vice President, AI

Senior Vice President AI/ML Software Engineer

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 and champions of inclusion, 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 Senior Vice President AI/ML Software Engineer to lead the architecture and delivery of production‑grade AI systems built on agentic frameworks, retrieval‑augmented generation (RAG), and LLM orchestration. This is a hands‑on technical leadership role responsible for a team of engineers building autonomous AI pipelines that extract, validate, and reason over complex unstructured documents. You will own the technical vision for a multi‑agent ecosystem – designing pipeline orchestration engines, embedding/vectorization strategies, knowledge retrieval systems, and AI‑assisted code generation tooling. You will lead a VP‑level engineer and a broader team of 4‑8 developers. This role is in New York, NY

What Sets This Role Apart

You build the agent framework, not just configure one – custom orchestration engine, not a LangChain wrapper – Production AI with real consequences – extraction accuracy directly impacts financial operations – Full RAG ownership – from raw OCR bytes through embedding, retrieval, and generation – Evaluation‑driven culture – golden‑truth datasets, automated regression, measurable quality gates – Greenfield AI + enterprise integration – build new AI‑native systems that plug into established platforms

In this role, you'll have the opportunity to impact on our organization in the following ways:

Technical Leadership & Architecture

Architect agentic AI systems: multi‑agent orchestration, tool‑use patterns, planning/reasoning loops, and autonomous decision chains – Design and evolve RAG infrastructure – chunking strategies, embedding pipelines, vector store selection, retrieval ranking, and context window optimization – Define vectorization strategy: embedding model selection, dimensionality trade‑offs, hybrid search (dense + sparse), and re‑ranking approaches – Own the AI pipeline orchestration framework – blocks, inlets/outlets, blackboards, memory stores, and content policy enforcement – Make build‑vs‑buy decisions across the AI toolchain (vector databases, agent frameworks, evaluation harnesses, model gateways) – Establish patterns for prompt engineering at scale: prompt versioning, chain‑of‑thought decomposition, few‑shot management, and guardrails

Agentic & RAG Systems

Design multi‑agent architectures with shared memory, blackboard patterns, and inter‑agent communication protocols – Build autonomous extraction agents capable of planning, tool selection, self‑correction, and validation – Implement knowledge graph construction from unstructured documents – entity extraction, relationship mapping, and graph‑based retrieval – Develop evaluation frameworks: retrieval precision/recall, extraction accuracy, agent task completion rates, and hallucination detection – Design feedback loops: human‑in‑the‑loop correction, reinforcement from golden‑truth datasets, and continuous prompt refinement

Team Leadership

Lead, mentor, and grow a team of 4‑8 engineers (AI/ML, backend, full‑stack) – Directly manage a VP‑level AI engineer; provide technical guidance and career development – Drive architecture reviews, design sessions, and technical decision‑making – Own sprint planning, technical backlog, and delivery commitments – Foster a culture of rapid experimentation balanced with production rigor

Hands‑On Engineering -

Implement core agentic components: agent loops, tool registries, memory persistence, and reasoning traces – Build embedding pipelines – document preprocessing, chunk boundary detection, metadata enrichment, and vector index management – Develop scoring and validation systems (Bayesian confidence, cross‑agent consensus, golden‑truth comparison) – Contribute to platform services (Java/Spring Boot) and AI service layer (Python/FastAPI) – Build AI‑assisted developer tooling: code generation workflows, automated test generation, and intelligent code review

Delivery & Operations

Own CI/CD pipelines, containerized deployments, and environment promotion – Define observability: agent execution traces, token usage tracking, retrieval quality metrics, and pipeline t