Applied AI Engineer

Information Security Responsibilities

• Promote and enforce awareness of key information security practices, including acceptable use of information assets, malware protection, and password security protocols
• Identify, assess, and report security risks, focusing on how these risks impact the confidentiality, integrity, and availability of information assets
• Understand and evaluate how data is stored, processed, or transmitted, ensuring compliance with data privacy and protection standards (GDPR, CCPA, etc.)
• Ensure data protection measures are integrated throughout the information lifecycle to safeguard sensitive information

Responsibilities:

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.

Requirements:

• 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)