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# Senior Vice President, AI / Machine Learning Software Engineer

- **Company:** The Bank of New York Mellon Corporation
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), A/B Testing, Artificial Intelligence, Automation of Tests, Microsoft Azure, Code Generation, Code Review, Encodings, Communications Protocols, Computer Engineering, D3.Js, Information Engineering, Data Stores, Entity Relationship Models, Graph Database, Hazelcast, Python (Programming Language), PostgreSQL, Machine Learning, Apache Maven, Named Entity Recognition, Scrum Methodology, Regression Testing, Service Layer, Software Engineering, TypeScript, Management of Software Versions, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Spring-boot, Caching, Backend, Usage Tracking, Fastapi, Pytest, Containerization, AngularJS, Gitlab-ci, Blackboard, Enterprise Integration, Machine Learning Operations, Front End Software Development, Api Design, Code Restructuring, Docker, Service Stack, Artifactory, Microservices
- **Published:** August 27, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18081135?backUrl=%2Fcareer%2F18081135%2FSenior-Vice-President-Ai-Machine-Learning-Software-Engineer-New-York-New-York

## About the Role

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

Preferred Qualifications

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

Technology Stack

AI/Agentic- LLM orchestration, multi-agent systems, ReAct patterns, tool-use, autonomous pipelines

RAG & Vectors- Embedding models, vector stores, hybrid search, re-ranking, chunk optimization

LLM- Azure OpenAI, GPT-4o, enterprise model gateways, prompt versioning

Python- Python 3.12/3.13, FastAPI, Poetry, Pydantic, async pipelines

Java- Java 21, Spring Boot 3.x, Maven, Resilience4j, Hazelcast

Frontend- Angular 19, TypeScript, D3.js, ECharts

Database- Oracle, PostgreSQL, vector databases

Infrastructure- Docker, GitLab CI/CD, Artifactory

Observability- Agent traces, token tracking, retrieval quality metrics, audit pipelines

## Description

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

Delivery & Operations

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


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