Senior Vice President, AI / ML Software Engineer

BNY Mellon•New York, NY

1d•$120,000 - $209,000•Onsite

About The Position

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

Requirements

Bachelor's degree or Advanced degree in computer science engineering or a related discipline, or equivalent work experience required.
10+ years of professional software engineering experience
3+ years leading or technically mentoring engineering teams
Deep expertise in AI/ML systems: LLM orchestration, prompt engineering, chain-of-thought reasoning
RAG architectures: chunking, embedding, retrieval, re-ranking, context assembly
Agentic patterns: ReAct, tool-use, planning loops, multi-agent coordination
Vector databases and embedding models (OpenAI embeddings, sentence-transformers, FAISS, Pinecone, Weaviate, or similar)
Strong Python (3.11+): FastAPI, async/await, Poetry, Pydantic, pytest
Solid Java experience: Java 21, Spring Boot 3.x, microservice architecture
Production AI delivery: not just prototypes -- systems handling real workloads with observability, error recovery, and audit trails
Document intelligence: OCR pipelines, NLP, structured extraction from unstructured text
Testing & evaluation: golden-truth validation, retrieval metrics (MRR, NDCG), extraction F1 scores, agent success rates
Enterprise architecture: API design, circuit breakers, caching, event-driven patterns

Nice To Haves

Experience building custom agent frameworks (not just using LangChain/CrewAI out-of-the-box)
Knowledge of graph-based retrieval -- knowledge graphs, graph RAG, entity-relationship extraction
Experience with code AI: AI-assisted development tools, code generation pipelines, automated refactoring
Familiarity with model fine-tuning, LoRA/QLoRA, or RLHF techniques
Exposure to evaluation-driven development -- automated prompt regression testing, A/B testing of retrieval strategies
Angular/TypeScript experience for full-stack visibility
Capital markets or financial services domain knowledge
Familiarity with enterprise AI governance: content policies, PII handling, data residency

Responsibilities

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
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
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
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
Own CI/CD pipelines, containerized deployments, and environment promotion
Define observability: agent execution traces, token usage tracking, retrieval quality metrics, and pipeline telemetry
Manage schema evolution and data stores (relational + vector)
Coordinate cross-team dependencies with platform engineering, data engineering, and infrastructure