AI Engineer

Applied AI Engineer

New York City -Need locals

What You''ll Do

·  Design and evolve reusable GenAI workflows used across Lending business lines.

·  Develop an enterprise grade AI-based document ingestion and data extraction capability, including traceability, confidence scoring, and human-in-the-loop review.

·  Build AI-powered assistants embedded in Lending systems using agentic workflows.

·  Deliver automated content and deck generation workflows for reporting and approvals.

·  Provide expert advice on GenAI architecture including model selection, orchestration patterns, and evaluation strategy.

·  Establish LLMOps practices: extraction accuracy, assistant reliability, prompts management, and audit monitoring.

·  Design and implement controls for entitlements, PII handling within open-source models in a regulated environment.

·  In the role you are expected to act as a hands-on technical expert, and it has a clear path to becoming a platform owner responsible for shared GenAI standards across Lending.

What You''ll Bring

·  2+, dedicated experience in practical application of GenAI solutions in an enterprise business environment. Designing and operating GenAI orchestration frameworks in production beyond vendor examples (e.g., LangChain systems).

·  5+ years of strong front-to-back engineering experience, focusing on AI/ML platforms and workflows (Python or Java).

·  Proven experience building and operating production-grade GenAI / LLM platforms, applying patterns such as RAG, tool/function calling, agentic workflows, and validated structured outputs.

·  Strong LLMOps expertise, including evaluation harnesses, prompt and version management, regression testing, observability, and reliability measurement in production systems.

·  Hands-on experience building AI-first data ingestion pipelines with measurable quality, accuracy, and reliability.

·  Advanced retrieval experience: advanced vector search, including multi-vector and late-interaction approaches (e.g., ColBERT, chunking), multi-stage retrieval pipelines, metadata filtering, re-ranking. Solid understanding of evaluation metrics and how they shape practical RAG system design (e.g., recall vs precision, latency vs quality, MRR, NDCG).

·  Experience operating GenAI systems through real production failures (model regressions, retrieval degradation, prompt drift, data quality issues) and designing mitigation strategies.

Nice to Have

·  Fixed Income or Institutional Lending domain experience.

• Experience working in regulated environments with strong audit and control requirements.
• Familiarity with enterprise security, data governance, and entitlement models.
• Experience designing reusable internal platforms or shared developer tooling.
• Frontend experience is beneficial (Angular or React).