Senior Vice President, AI​/Machine Learning Software Engineer

JOB DESCRIPTION

Senior 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.

• You build the agent framework, not just configure one -- custom orchestration engine, not a Lang Chain wrapper
• 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…