Vice President, AI​/ML Software Engineer

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

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 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 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:

AI Systems Development

Implement 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 tooling

Platform & Backend Engineering

Build 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 pipelines

Mentorship & Collaboration

Mentor 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 solutions

To 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 assembly

Agentic AI experience: Agent orchestration (custom frameworks, Lang Graph, 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…