Senior
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
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
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
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:
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
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
AI/Agentic
RAG & Vectors
LLM
Python
Java
Frontend
Database
Infrastructure
Observability
BNY offers highly competitive compensation, benefits, and wellbeing programs rooted in a strong culture of excellence and our pay-for-performance philosophy. We provide access to flexible global resources and tools for your life’s journey. Focus on your health, foster your personal resilience, and reach your financial goals as a valued member of our team, along with generous paid leaves, including paid volunteer time, that can support you and your family through moments that matter.
BNY is an Equal Employment Opportunity/Affirmative Action Employer - Underrepresented racial and ethnic groups/Females/Individuals with Disabilities/Protected Veterans.
This position is at-will and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation) at any time, including for reasons related to individual performance, change in geographic location, Company or individual department/team performance, and market factors.
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