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BNY

Safeguards and moves assets for investors

AI/ML Software Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USA$120k - $209kIn-person

About the Job

Requirements

  • A bachelor's degree or advanced degree in computer science engineering or a related discipline, or equivalent work experience, is required.
  • At least 10 years of professional software engineering experience is required.
  • At least 3 years leading or technically mentoring engineering teams is required.
  • Deep expertise in AI/ML systems, including LLM orchestration, prompt engineering, and chain-of-thought reasoning, is required.
  • Expertise in RAG architectures, including chunking, embedding, retrieval, re-ranking, and context assembly, is required.
  • Expertise in agentic patterns, including ReAct, tool use, planning loops, and multi-agent coordination, is required.
  • Experience with vector databases and embedding models such as OpenAI embeddings, sentence-transformers, FAISS, Pinecone, Weaviate, or similar is required.
  • Strong Python (3.11+) skills, including FastAPI, async/await, Poetry, Pydantic, and pytest, are required.
  • Solid Java experience, including Java 21, Spring Boot 3.x, and microservice architecture, is required.
  • Experience delivering production AI systems that handle real workloads with observability, error recovery, and audit trails is required.
  • Experience with document intelligence, including OCR pipelines, natural language processing, and structured extraction from unstructured text, is required.
  • Experience with testing and evaluation, including golden-truth validation, retrieval metrics (MRR, NDCG), extraction F1 scores, and agent success rates, is required.
  • Enterprise architecture experience, including API design, circuit breakers, caching, and event-driven patterns, is required.

Responsibilities

  • Architect agentic AI systems, including multi-agent orchestration, tool-use patterns, planning and reasoning loops, and autonomous decision chains.
  • Design and evolve RAG infrastructure, including chunking strategies, embedding pipelines, vector store selection, retrieval ranking, and context window optimization.
  • Define vectorization strategy, including embedding model selection, dimensionality trade-offs, hybrid search, and re-ranking approaches.
  • Own the AI pipeline orchestration framework, including blocks, inlets/outlets, blackboards, memory stores, and content policy enforcement.
  • Make build-versus-buy decisions across the AI toolchain, including vector databases, agent frameworks, evaluation harnesses, and model gateways.
  • Establish scalable prompt engineering patterns, including 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, including entity extraction, relationship mapping, and graph-based retrieval.
  • Develop evaluation frameworks for retrieval precision and recall, extraction accuracy, agent task completion rates, and hallucination detection.
  • Design feedback loops using human-in-the-loop correction, reinforcement from golden-truth datasets, and continuous prompt refinement.
  • Lead, mentor, and grow a team of 4–8 engineers across AI/ML, backend, and full-stack disciplines.
  • Directly manage a VP-level AI engineer and 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, including agent loops, tool registries, memory persistence, and reasoning traces.
  • Build embedding pipelines, including document preprocessing, chunk boundary detection, metadata enrichment, and vector index management.
  • Develop scoring and validation systems using Bayesian confidence, cross-agent consensus, and golden-truth comparison.
  • Contribute to platform services using Java and Spring Boot and to the AI service layer using Python and FastAPI.
  • Build AI-assisted developer tooling, including code generation workflows, automated test generation, and intelligent code review.
  • Own CI/CD pipelines, containerized deployments, and environment promotion.
  • Define observability for agent execution traces, token usage tracking, retrieval quality metrics, and pipeline telemetry.
  • Manage schema evolution and relational and vector data stores.
  • Coordinate cross-team dependencies with platform engineering, data engineering, and infrastructure.

Desired Qualifications

  • Experience building custom agent frameworks rather than only using out-of-the-box LangChain or CrewAI.
  • Knowledge of graph-based retrieval, including knowledge graphs, graph RAG, and entity-relationship extraction.
  • Experience with code AI, including AI-assisted development tools, code generation pipelines, and automated refactoring.
  • Familiarity with model fine-tuning, LoRA/QLoRA, or RLHF techniques.
  • Exposure to evaluation-driven development, including automated prompt regression testing and A/B testing of retrieval strategies.
  • Angular and TypeScript experience for full-stack visibility.
  • Capital markets or financial services domain knowledge.
  • Familiarity with enterprise AI governance, including content policies, PII handling, and data residency.

About the Company

BNY

Safeguards and moves assets for investors

BNY safeguards financial assets and runs investment services for institutions, investment firms and wealthy clients around the world. Its custody business keeps clients' securities safe and handles the records and transactions needed to administer investment funds. Banks and brokers use its systems to settle trades, manage collateral and send payments, while its investment and wealth businesses manage portfolios and advise clients. It earns servicing and management fees as well as interest on loans and securities funded by client deposits. BNY traces its roots to the Bank of New York, founded by Alexander Hamilton in 1784, and is headquartered in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1784

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