Applied AI Engineer

Role : Applied AI Engineer

Location: NYC, NY

Overview

STAFFXPERT is seeking a GenAI workflow platform to enable document data extraction, embedded productivity assistants, and automated business workflows across business lines. This is not a research or demo role. We are seeking senior hands-on full stack engineers who have designed, built, and operated GenAI systems in production, and understand failure modes, evaluation, and governance as first-class AI-powered systems.

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).