New York, NY, United States
Live, first seen by us 80 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Architect multi-agent AI systems and RAG infrastructure
Lead, mentor, and grow a team of 4-8 engineers
Build embedding pipelines and vector index management
Develop evaluation frameworks for retrieval and extraction accuracy
Own CI/CD pipelines, containerized deployments, and observability
Architect agentic AI systems: multi-agent orchestration, tool-use patterns, planning/reasoning loops, and autonomous decision chains
Lead, mentor, and grow a team of 4-8 engineers (AI/ML, backend, full-stack)
Build embedding pipelines -- document preprocessing, chunk boundary detection, metadata enrichment, and vector index management
Develop evaluation frameworks: retrieval precision/recall, extraction accuracy, agent task completion rates, and hallucination detection
Own CI/CD pipelines, containerized deployments, and environment promotion
Python- Python 3.12/3.13, FastAPI, Poetry, Pydantic, async pipelines
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Day 80
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Well past BNY Mellon’s usual: its jobs come down by about day 27.
From 51 similar postings that came down, and 101 of BNY Mellon’s. Coming down is not always a hire.
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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
To be successful in this role, we’re seeking the following: 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
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
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
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
Java- Java 21, Spring Boot 3.x, Maven, Resilience4j, Hazelcast Frontend- Angular 19, TypeScript, D3.js, ECharts Database- Oracle, PostgreSQL, vector databases
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
Database- Oracle, PostgreSQL, vector databases Infrastructure- Docker, GitLab CI/CD, Artifactory Observability- Agent traces, token tracking, retrieval quality metrics, audit pipelines
Frontend- Angular 19, TypeScript, D3.js, ECharts Database- Oracle, PostgreSQL, vector databases Infrastructure- Docker, GitLab CI/CD, Artifactory
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We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
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.
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
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
To be successful in this role, we’re seeking the following:
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
Our Benefits and Rewards:
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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New York, NY, United States
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
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
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