Lead AI ML Engineer

UFS LLC • Remote, US,

11h • Hybrid

About The Position

The Lead AI/ML Engineer owns the brain of the Navanta AI platform — retrieval, text-to-metrics, model serving, tool orchestration, and the evaluation harness that keeps answers honest. Working under the SVP of Technology and Commercial AI and in close collaboration with the data, platform, and product teams, this role makes “correct and verifiable” the product’s default — the foundation of trust in a regulated banking environment where a confident wrong number loses the account.

Requirements

• 6–10+ years building software, with 2–3+ years shipping production LLM, RAG, or NLP systems used by real people — not prototypes
• A demonstrated focus on accuracy and evaluation, not just demos
• Strong Python and solid software-engineering fundamentals
• Comfort operating self-hosted open-weight models and reasoning about latency, cost, and quality trade-offs
• Bachelor’s degree in computer science, mathematics, or a related technical field, or equivalent hands-on experience

Nice To Haves

• Experience in regulated or high-stakes domains where a wrong answer is costly
• Fine-tuning, adapters, and retrieval-quality optimization
• Familiarity with banking and finance terminology
• Experience in the financial services industry or a regulated, high-accuracy AI application environment strongly preferred

Responsibilities

• Build Navanta’s retrieval and verifications over data systems, with shown queries and citations for every answer
• Stand up self-hosted open-weight models serving and embeddings inside each bank’s environment or shared environments for Navanta; evolve RAG to a dedicated standard
• Design the MCP tool layer that exposes a small, audited set of read-only tools (metrics, documents, customer 360), eventually growing into read/write tools with heavy amounts of regulated, highly sensitive data
• Build and maintain the evaluation harness — golden-question regression, groundedness and retrieval metrics, explicit “I don’t know” behavior — and make it a release gate
• Implement LLM guardrails: PII redaction in prompts and context, prompt-injection defenses, and cost and row limits aligned to regulatory security expectations
• Partner with data teams so the model selects governed metrics from the semantic layer rather than improvising SQL
• Document model architecture, evaluation methodology, and guardrail controls to support customer security reviews and audit readiness
• Track latency, cost, and quality trade-offs across model versions and deployment configurations

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree