Lead AI ML Engineer

UFS Tech

Lead AI/ML Engineer developing the Navanta AI platform by building retrieval systems and evaluation metrics. Collaborate with product teams, ensuring compliance and groundedness in banking environments.

Posted 7/16/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in building and maintaining LLM, RAG, and NLP systems with a strong emphasis on accuracy, evaluation, and regulatory compliance. Proficient in Python and capable of operating self-hosted open-weight models while managing latency, cost, and quality trade-offs.

Highest-signal resume keywords

LLM DevelopmentRAG ImplementationNLP SystemsPython ProgrammingSoftware Engineering Fundamentals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM DevelopmentRAG ImplementationNLP SystemsPython ProgrammingSoftware Engineering Fundamentals

Soft Skills

Focus on AccuracyEvaluation Methodology

Tools & Technologies

Self-Hosted Open-Weight ModelsMCP Tool LayerEvaluation Harness

Industry Keywords

Data SystemsRegulatory SecurityPII RedactionAudit Readiness

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

Typical office environment
Up to 20% travel time may be required