Vice President, AI/ML Software Engineer

Vice President AI/ML Software EngineerAt 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 are seeking a Vice President AI/ML Software Engineer to design and implement agentic AI systems, RAG pipelines, and intelligent document processing services. This is a senior individual contributor role with high autonomy -- you will own significant components of our AI platform, from embedding pipelines and vector retrieval to multi-agent extraction workflows.

You will work closely with the SVP lead to translate architectural vision into production code, while mentoring mid-level engineers and driving technical excellence across the team. 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 platformsIn this role, you'll have the opportunity to impact on our organization in the following ways:AI Systems DevelopmentImplement agentic pipelines: agent loops, tool registries, memory stores, reasoning traces, and self-correction mechanisms - Build and optimize RAG systems end-to-end: - Document ingestion and preprocessing (OCR output, PDFs, structured/unstructured text) - Chunking strategies (section-aware, semantic, sliding window, hierarchical) - Embedding generation and vector index management - Retrieval orchestration: hybrid search, metadata filtering, re-ranking - Context assembly and prompt construction for downstream LLM calls - Develop vectorization pipelines -- embedding model integration, batch processing, incremental index updates, and similarity search tuning - Implement multi-agent coordination patterns: shared blackboards, inter-agent messaging, task decomposition, and consensus mechanisms - Build prompt engineering infrastructure: template management, few-shot example selection, chain-of-thought scaffolding, and output parsing - Develop evaluation harnesses: automated accuracy measurement, retrieval quality metrics, regression detection, and A/B comparison toolingPlatform & Backend EngineeringBuild FastAPI services exposing AI capabilities as production APIs (extraction, validation, classification) - Contribute to Java/Spring Boot platform services where AI integrates with business workflow - Design and maintain database schemas for AI metadata: audit trails, pipeline runs, memory entries, knowledge graphs - Implement content policy enforcement and data governance controls within AI pipelinesMentorship & CollaborationMentor 2-3 mid-level engineers on AI engineering practices - Participate in architecture reviews and design sessions - Document patterns, decisions, and runbooks for AI system operation - Collaborate with product and business stakeholders to translate requirements into technical solutionsTo be successful in this role, we're seeking the following:Bachelor's degree in Computer Science, Engineering, or related field. - Advanced degree preferred.

6+ years of professional software engineering experience - 2+ years building production AI/ML systems (not just notebooks/prototypes. Strong problem-solving skills with the ability to manage complex data processes. - Excellent collaboration and communication skills to work effectively with cross-functional teams.Strong RAG expertise: Embedding models (OpenAI, sentence-transformers, Cohere, or similar) - Vector databases (FAISS, Pinecone, Weaviate, Chroma, pgvector, or similar) - Chunking and retrieval optimization - Context window management and prompt assemblyAgentic AI experience: Agent orchestration (custom frameworks, LangGraph, or similar) - Tool-use patterns, function calling, structured output parsing - Memory and state management for multi-turn agent interactions - Python proficiency (3.11+): FastAPI, async patterns, Pydantic, Poetry, pytest - LLM integration: prompt engineering, token management, streaming, error handling, rate limiting - NLP & document processing: OCR post-processing, text segmentation, entity extraction - Testing rigor: unit tests, integration tests, golden-truth validation, retrieval metric evaluation - API design: RESTful services, OpenAPI specifications, versioning strategiesPreferred QualificationsExperience with knowledge graph construction from unstructured text - Familiarity with code AI concepts: code generation, automated testing, AI-assisted refactoring - Java/Spring Boot experience for cross-stack contribution - Angular/TypeScript for full-stack context - Experience with model evaluation: F1 scores, precision/recall for extraction, MRR/NDCG for retrieval - Exposure to fine-tuning or prompt optimization techniques - Understanding of graph RAG or hybrid retrieval architectures - Capital markets or financial services domain exposure - Experience with enterprise deployment: Docker, CI/CD, artifact repositoriesTechnology StackAI/Agentic: Multi-agent pipelines, tool-use, autonomous extraction, reasoning loopsRAG: Embedding models, vector stores, hybrid search, chunking, re-rankingLLM: Azure OpenAI, GPT-4o, structured outputs, function callingPython: Python 3.12/3.13, FastAPI, Poetry, Pydantic, Gunicorn/UvicornJava: Java 21, Spring Boot 3.x (contributory)NLP/OCR: Azure Document Intelligence, NLTK, document graph parsingDatabase: Oracle, PostgreSQL, vector databasesInfrastructure: Docker, GitLab CI/CDTesting: Pytest, golden-truth validation, retrieval metrics, evaluation harnesses