Senior Vice President, AI/ML Software Engineer

<div><h3>Senior Vice President AI/ML Software Engineer</h3><br /><p>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.</p><br /><p>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 all about. Join us and be part of something extraordinary.</p><br /><p>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.</p><br /><h3>What Sets This Role Apart</h3><br /><ul><br /><li>You build the agent framework, not just configure one -- custom orchestration engine, not a LangChain wrapper</li><br /><li>Production AI with real consequences -- extraction accuracy directly impacts financial operations</li><br /><li>Full RAG ownership -- from raw OCR bytes through embedding, retrieval, and generation</li><br /><li>Evaluation-driven culture -- golden‑truth datasets, automated regression, measurable quality gates</li><br /><li>Greenfield AI + enterprise integration -- build new AI‑native systems that plug into established platforms</li><br /></ul><br /><h3>In this role, you'll have the opportunity to impact on our organization in the following ways:</h3><br /><h3>Technical Leadership &amp; Architecture</h3><br /><ul><br /><li>Architect agentic AI systems: multi‑agent orchestration, tool‑use patterns, planning/reasoning loops, and autonomous decision chains</li><br /><li>Design and evolve RAG infrastructure — chunking strategies, embedding pipelines, vector store selection, retrieval ranking, and context window optimization</li><br /><li>Define vectorization strategy: embedding model selection, dimensionality trade‑offs, hybrid search (dense + sparse), and re‑ranking approaches</li><br /><li>Own the AI pipeline orchestration framework — blocks, inlets/outlets, blackboards, memory stores, and content policy enforcement</li><br /><li>Make build‑vs‑buy decisions across the AI toolchain (vector databases, agent frameworks, evaluation harnesses, model gateways)</li><br /><li>Establish patterns for prompt engineering at scale: prompt versioning, chain‑of‑thought decomposition, few‑shot management, and guardrails</li><br /></ul><br /><h3>Agentic &amp; RAG Systems</h3><br /><ul><br /><li>Design multi‑agent architectures with shared memory, blackboard patterns, and inter‑agent communication protocols</li><br /><li>Build autonomous extraction agents capable of planning, tool selection, self‑correction, and validation</li><br /><li>Implement knowledge graph construction from unstructured documents — entity extraction, relationship mapping, and graph‑based retrieval</li><br /><li>Develop evaluation frameworks: retrieval precision/recall, extraction accuracy, agent task completion rates, and hallucination detection</li><br /><li>Design feedback loops: human‑in‑the‑loop correction, reinforcement from golden‑truth datasets, and continuous prompt refinement</li><br /></ul><br /><h3>Team Leadership</h3><br /><ul><br /><li>Lead, mentor, and grow a team of 4‑8 engineers (AI/ML, backend, full‑stack)</li><br /><li>Directly manage a VP‑level AI engineer; provide technical guidance and career development</li><br /><li>Drive architecture reviews, design sessions, and technical decision‑making</li><br /><li>Own sprint planning, technical backlog, and delivery commitments</li><br /><li>Foster a culture of rapid experimentation balanced with production rigor</li><br /></ul><br /><h3>Hands‑On Engineering</h3><br /><ul><br /><li>Implement core agentic components: agent loops, tool registries, memory persistence, and reasoning traces</li><br /><li>Build embedding pipelines — document preprocessing, chunk boundary detection, metadata enrichment, and vector index management</li><br /><li>Develop scoring and validation systems (Bayesian confidence, cross‑agent consensus, golden‑truth comparison)</li><br /><li>Contribute to platform services (Java/Spring Boot) and AI service layer (Python/FastAPI)</li><br /><li>Build AI‑assisted developer tooling: code generation workflows, automated test generation, and intelligent code review</li><br /></ul><br /><h3>Delivery &amp; Operations</h3><br /><ul><br /><li>Own CI/CD pipelines, containerized deployments, and environment promotion</li><br /><li>Define observability: agent execution traces, token usage tracking, retrieval quality metrics, and pipeline telemetry</li><br /><li>Manage schema evolution and data stores (relational + vector)</li><br /><li>Coordinate cross‑team dependencies with platform engineering, data engineering, and infrastructure</li><br /></ul><br /><h3>To be successful in this role, we’re seeking the following:</h3><br /><ul><br /><li>Bachelor's degree or Advanced degree in computer science engineering or a related discipline, or equivalent work experience required.</li><br /><li>10+ years of professional software engineering experience</li><br /><li>3+ years leading or technically mentoring engineering teams</li><br /><li>Deep expertise in AI/ML systems:<ul><br /><li>LLM orchestration, prompt engineering, chain‑of‑thought reasoning</li><br /><li>RAG architectures: chunking, embedding, retrieval, re‑ranking, context assembly</li><br /><li>Agentic patterns: ReAct, tool‑use, planning loops, multi‑agent coordination</li><br /><li>Vector databases and embedding models (OpenAI embeddings, sentence‑transformers, FAISS, Pinecone, Weaviate, or similar)</li><br /><li>Strong Python (3.11+): FastAPI, async/await, Poetry, Pydantic, pytest</li><br /><li>Solid Java experience: Java 21, Spring Boot 3.x, microservice architecture</li><br /><li>Production AI delivery: not just prototypes — systems handling real workloads with observability, error recovery, and audit trails</li><br /></ul></li></ul></div><br />#J-18808-Ljbffr