Applied AI Engineer - GenAI Platform Hybrid

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 who treat failure modes, evaluation, and governance as first-class concerns. The role is a hands-on technical expert seat with a clear path to becoming a platform owner responsible for shared GenAI standards across Lending.

What You''''ll Do

• Design and evolve reusable GenAI workflows used across Lending business lines.
• Build an enterprise-grade AI document ingestion and data extraction capability, including traceability, confidence scoring, and human-in-the-loop review.
• Develop AI-powered assistants embedded in Lending systems using agentic workflows.
• Deliver automated content and deck generation workflows for reporting and approvals.
• Advise on GenAI architecture: model selection, orchestration patterns, and evaluation strategy.
• Establish LLMOps practices covering extraction accuracy, assistant reliability, prompt management, and audit monitoring.
• Design and implement controls for entitlements and PII handling, including safe use of open-source models in a regulated environment.

• 6-7+ years of front-to-back engineering experience in Python or Java, with a focus on AI/ML platforms and workflows.
• 3+ years of dedicated, practical GenAI experience in an enterprise business environment, including designing and operating orchestration frameworks in production beyond vendor examples (e.g., custom LangChain-based systems).
• Proven experience building and operating production-grade GenAI/LLM platforms applying RAG, tool/function calling, agentic workflows, and validated structured outputs.
• Strong LLMOps expertise: evaluation harnesses, prompt and version management, regression testing, observability, and reliability measurement in production.
• Hands-on experience building AI-first data ingestion pipelines with measurable quality, accuracy, and reliability.
• Advanced retrieval depth: multi-vector and late-interaction approaches (e.g., ColBERT), chunking strategy, multi-stage retrieval pipelines, metadata filtering, and re-ranking - plus a working command of evaluation metrics (recall vs. precision, latency vs. quality, MRR, NDCG) and how they shape RAG design.
• Experience operating GenAI systems through real production failures - model regressions, retrieval degradation, prompt drift, data quality issues - and designing mitigations.

Nice to Have

• Fixed Income or Institutional Lending domain experience.
• Experience in regulated environments with strong audit and control requirements.
• Familiarity with enterprise security, data governance, and entitlement models.
• Experience building reusable internal platforms or shared developer tooling.
• Frontend experience (Angular or React).