Senior Vice President, AI / ML Software Engineer

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 

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