Lead AI/ML Engineer

Job not on LinkedIn

🕒 July 15

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 18%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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UFS Tech

WebsiteLinkedInAll Job Openings

201 - 500 employees

🏦 Banking

💳 Fintech

🔒 Cybersecurity

💰 $19.9M Venture Round - UFS on 2019-05

Banking • Fintech • Cybersecurity

UFS Tech is a technology and services provider focused on empowering community banks. It delivers core banking and digital banking platforms, payments, cash management, and customer engagement tools alongside managed IT services (private cloud, hosted email, end-user support), cybersecurity offerings (virtual firewall management, vulnerability management, SureProtect/SureWatch), backup and disaster recovery, document imaging and delivery, and advisory services for IT, information security, and regulatory compliance. UFS positions itself as a partner to community banks, offering purpose-built, modular solutions and professional services to help banks operate securely, meet regulatory requirements, and grow customer relationships.

📋 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