Help build an enterprise-grade GenAI workflow platform supporting document data extraction, integrated productivity assistants, and automated business processes across multiple lines of business.
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.
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.

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.

Bounteous is proud to be an equal opportunity employer.
Bounteous does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, physical or mental disability, national origin, veteran status, or any other status protected under federal, state, or local law.
Bounteous is willing to sponsor eligible candidates for employment visas.

Applied AI Engineer

Key skills