Senior Vice President, AI​/ML Software Engineer

Position: Senior Vice President, AI / ML Software Engineer
Job Description

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 Lang Chain 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,…