Lead AI ML Engineer

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UFS Tech

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 1991

🔒 Cybersecurity

💳 Fintech

💰 $19.9M Venture Round on 2019-05

Cybersecurity • IT Services • Fintech

UFS Tech is the Technology Outfitter for Community Banks, providing tailored technology solutions and IT managed services that empower community banks to compete effectively with larger national banks. With over 30 years of expertise, UFS Tech focuses on cybersecurity, virtual firewall management, core banking, digital banking, and compliance solutions to help banks manage their technology needs efficiently and securely. The company is committed to collaboration, innovation, and delivering purpose-built solutions tailored to the unique requirements of community banks.

📋 Description

• 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

• 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

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