Machine Learning Jobs in West Jordan, UT

Leads small-business loan underwriting operations by managing supervisors and team leads, developing frontline leaders, setting workflows and quality standards, and driving production performance. Oversees complex risk escalations, partners with Credit, Risk, Sales, Product, Compliance, and Operations, and manages workforce planning for the Utah office. Requires expertise in SMB credit analysis, financial statements, risk assessment, automated decisioning, compliance, and high-volume technology-enabled lending.

Own and execute U.S. fraud strategy across onboarding, account linking, and payment flows. Design, deploy, and optimize real-time fraud rules, post-transaction monitoring, risk scores, and machine learning models. Lead fraud investigations, root-cause analyses, policy and control development, KPI reporting, and fraud exposure analysis. Partner with Product, Engineering, Compliance, Data, Payments, and Operations to reduce losses, manage customer friction, and improve approval rates.

Owns strategy, roadmap, and execution for KYC data intelligence products supporting financial crime risk management. Leads customer discovery, requirements, prioritization, development, launch, adoption, and optimization while partnering with engineering, data, sales, marketing, services, customers, and partners. The role requires enterprise SaaS product management expertise, technical fluency, commercial judgment, and familiarity with KYC, AML, AI, analytics, APIs, and regulated financial services.

Define and support AI products for public safety and justice workflows. Conduct user discovery with first responders, dispatchers, and legal professionals; create product requirements and technical specifications; analyze competitors and APIs; monitor generative AI, computer vision, and drone-response trends; and prioritize roadmaps based on impact, feasibility, KPIs, privacy, ethics, and justice-sector regulations.

Lead the technical direction for scalable, cloud-native, API-first products and internal platforms. Design, build, test, deploy, monitor, and optimize distributed systems; contribute to conversational AI capabilities; improve CI/CD and engineering workflows; resolve performance issues; and provide technical leadership across architecture, coding standards, project execution, and team collaboration.

Leads AI, machine learning, and Generative AI engagements from opportunity discovery through solution design, prototyping, evaluation, and deployment readiness. Responsibilities include architecture, RAG and agent development, model evaluation, prompt engineering, governance, monitoring, documentation, client advising, proposal support, and team coaching. The role requires balancing hands-on technical leadership, responsible AI practices, commercial judgment, and stakeholder communication across complex client environments.

Leads AI, machine learning, and Generative AI engagements from opportunity discovery through solution design, evaluation, deployment readiness, governance, and adoption. Designs and reviews RAG assistants, document intelligence, agents, and analytics applications; advises on models, platforms, architecture, responsible AI, MLOps, and LLMOps. Partners with senior clients, manages technical quality and delivery workstreams, develops proposals and reusable assets, and coaches junior team members.

Leads customer-facing AI pre-sales engagements and designs tailored enterprise AI architectures. Advises executive stakeholders on AI strategy, ROI, governance, secure LLM integration, RAG, cloud deployment, networking, datacenter infrastructure, and conversational or agentic AI. Partners with sales, engineering, and technical domain teams to evolve AI offerings, guide digital transformation, and promote scalable, ethical, high-performance deployments.

Lead a team to design, develop, test, implement, and optimize AML transaction monitoring scenarios. Manage the full scenario lifecycle, collaborate with stakeholders (Product, Technology, Decision Science, Model Risk), evaluate ML opportunities, govern project intake, and report on scenario performance and KRIs/KPIs. Continuously improve detection coverage, efficiency, and documentation to support SAR processes.