In this role, you'll have the opportunity to impact on our organization in the following ways:
AI Systems Development
Implement agentic pipelines: agent loops, tool registries, memory stores, reasoning traces, and self-correction mechanisms - Build and optimize RAG systems end-to-end: - Document ingestion and preprocessing (OCR output, PDFs, structured/unstructured text) - Chunking strategies (section-aware, semantic, sliding window, hierarchical) - Embedding generation and vector index management - Retrieval orchestration: hybrid search, metadata filtering, re-ranking - Context assembly and prompt construction for downstream LLM calls - Develop vectorization pipelines -- embedding model integration, batch processing, incremental index updates, and similarity search tuning - Implement multi-agent coordination patterns: shared blackboards, inter-agent messaging, task decomposition, and consensus mechanisms - Build prompt engineering infrastructure: template management, few-shot example selection, chain-of-thought scaffolding, and output parsing - Develop evaluation harnesses: automated accuracy measurement, retrieval quality metrics, regression detection, and A/B comparison tooling
Platform & Backend Engineering
Build FastAPI services exposing AI capabilities as production APIs (extraction, validation, classification) - Contribute to Java/Spring Boot platform services where AI integrates with business workflow - Design and maintain database schemas for AI metadata: audit trails, pipeline runs, memory entries, knowledge graphs - Implement content policy enforcement and data governance controls within AI pipelines
Mentorship & Collaboration
Mentor 2-3 mid-level engineers on AI engineering practices - Participate in architecture reviews and design sessions - Document patterns, decisions, and runbooks for AI system operation - Collaborate with product and business stakeholders to translate requirements into technical solutions
To be successful in this role, we’re seeking the following
Bachelor's degree in Computer Science, Engineering, or related field. - Advanced degree preferred. 6+ years of professional software engineering experience - 2+ years building production AI/ML systems (not just notebooks/prototypes. Strong problem-solving skills with the ability to manage complex data processes. - Excellent collaboration and communication skills to work effectively with cross-functional teams. )
Strong RAG expertise: Embedding models (OpenAI, sentence-transformers, Cohere, or similar) - Vector databases (FAISS, Pinecone, Weaviate, Chroma, pgvector, or similar) - Chunking and retrieval optimization - Context window management and prompt assembly
Agentic AI experience: Agent orchestration (custom frameworks, LangGraph, or similar) - Tool-use patterns, function calling, structured output parsing - Memory and state management for multi-turn agent interactions - Python proficiency (3.11+): FastAPI, async patterns, Pydantic, Poetry, pytest - LLM integration: prompt engineering, token management, streaming, error handling, rate limiting - NLP & document processing: OCR post-processing, text segmentation, entity extraction - Testing rigor: unit tests, integration tests, golden-truth validation, retrieval metric evaluation - API design: RESTful services, OpenAPI specifications, versioning strategies
Preferred Qualifications
Experience with knowledge graph construction from unstructured text - Familiarity with code AI concepts: code generation, automated testing, AI-assisted refactoring - Java/Spring Boot experience for cross-stack contribution - Angular/TypeScript for full-stack context - Experience with model evaluation: F1 scores, precision/recall for extraction, MRR/NDCG for retrieval - Exposure to fine-tuning or prompt optimization techniques - Understanding of graph RAG or hybrid retrieval architectures - Capital markets or financial services domain exposure - Experience with enterprise deployment: Docker, CI/CD, artifact repositories
Technology Stack
AI/Agentic: Multi-agent pipelines, tool-use, autonomous extraction, reasoning loops
RAG: Embedding models, vector stores, hybrid search, chunking, re-ranking
LLM: Azure OpenAI, GPT-4o, structured outputs, function calling
Python: Python 3.12/3.13, FastAPI, Poetry, Pydantic, Gunicorn/Uvicorn
Java: Java 21, Spring Boot 3.x (contributory)
NLP/OCR: Azure Document Intelligence, NLTK, document graph parsing
Database: Oracle, PostgreSQL, vector databases
Infrastructure: Docker, GitLab CI/CD
Testing: Pytest, golden-truth validation, retrieval metrics, evaluation harnesses
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.
