AI Engineer

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

• Provide governance, oversight, and controlled expansion of internal AI tool
• Execute quarterly, risk-based testing program for independent credible challenge
• Collaborate with various stakeholders to review AI initiatives and escalate risks
• Evaluate controls across the AI lifecycle
• Translate regulatory requirements into AI-focused testing procedures
• Develop credible challenge documentation and reporting for governance committees
• Enhance AI testing frameworks and tools

Lead AI Compliance Testing

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

Enterprise AI Architect

• Define the AI engineering roadmap and architecture standards across the organization
• Lead build vs. buy vs. integrate decision-making for AI systems and platforms
• Architect AI solutions for the enterprise platform
• Design systems for scale, reliability, and cost-efficiency in production environments
• Establish LLMOps practices
• Partner closely with Product and Engineering leadership to align AI capabilities with business outcomes
• Champion and introduce AI-assisted coding practices and developer tooling across the engineering organization

Lead AI Engineer

• Define the AI engineering roadmap and architecture standards across the organization
• Lead build vs. buy vs. integrate decision-making for AI systems and platforms – and be accountable for articulating how you arrived at those decisions
• Evaluate emerging tools, frameworks, and models; provide clear, defensible recommendations to leadership
• Serve as the ultimate technical escalation point for complex AI/ML system design challenges
• Architect AI solutions for our enterprise platform
• Design systems for scale, reliability, and cost-efficiency in production environments
• Establish LLMOps practices
• Ensure AI systems integrate cleanly with existing product and engineering infrastructure
• Partner closely with Product and Engineering leadership to align AI capabilities with business outcomes
• Receive operational support to help initiate and coordinate cross-functional engagement
• Provide clear guidance on resource allocation
• Translate complex technical concepts for non-technical audiences
• Champion and introduce AI-assisted coding practices and developer tooling across the engineering organization
• Define standards for responsible AI development including guardrails, evaluation frameworks, and security considerations
• Drive continuous improvement in how the organization builds and ships AI-powered features

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Use Claude Code to implement new features and functions within the platform
• Translate product requirements and design specs into clean, production-ready code
• Review and refine AI-generated code to ensure correctness, performance, and maintainability
• Leverage AI to write and update automated tests for all new functionality
• Participate in async code reviews and provide constructive feedback
• Ensure new features, APIs, and architectural decisions are documented as they are built
• Proactively identify technical debt or gaps and surface them to the team

AI Software Developer

• Leading, mentoring, and growing a global team of software engineers and Forward Deployed Engineers, fostering a high-performance, artificial intelligence–first culture.
• Driving adoption of artificial intelligence across the software development lifecycle to improve engineering productivity, quality, and velocity.
• Managing team execution, delivery, hiring, performance management, and career development while ensuring operational excellence.
• Partnering with Product Management and senior engineering leaders to define and deliver scalable artificial intelligence agent platforms and enterprise capabilities.
• Designing and contributing to cloud-native solutions leveraging Microsoft Azure, Python, and C# technologies.
• Building and leading a Forward Deployed Engineering function that collaborates directly with customers to validate use cases and accelerate artificial intelligence adoption.
• Translating customer feedback and real-world implementations into reusable platform features and product enhancements.

Product Development Manager – AI Platform

• Design, develop, and maintain robust, scalable, and high-performance object oriented code in our backend services.
• Develop public REST APIs using Java and internal gRPC APIs for inter-service and inter-system communication.
• Craft systems designs, lead design decisions, and drive alignment with other senior engineers.
• Write automated unit tests, integration tests, end-to-end tests, concurrency tests, load/performance tests.
• Analyze existing systems to identify bottlenecks, tech debt, and implement scalability, and stability improvements.
• Implement automation for testing, monitoring, healing, and scaling applications, continuous integration and deployment to reduce time to market.
• Collaborate with cross-functional teams, including product managers, designers, and other engineers, to define and implement new features.
• Conduct code reviews (comment, approve, seek revisions, merge), mentor junior and mid-level engineers, and actively promote engineering best practices.
• Dive deep and troubleshoot complex issues, devise fixes, author root cause analysis documents, and ensure lasting performance and reliability.
• Conduct objective and comparative analyses of competing technologies to advise the team of pros and cons of a technology solution.
• Maintain robust documentation (design docs, run books, change management docs, and readiness plans).
• Provide live-site support for production applications by monitoring systems, ensuring rapid incident resolution, and driving continuous improvement.
• Drive cross-team projects as a single-threaded-owner (STO) or tech lead, and actively unblock other engineers to make progress.
• Design and build agentic AI systems and services, enabling autonomous workflows, reasoning, and task execution within Mobility platforms.
• Develop AI agents from scratch, including orchestration, tool usage, memory, and multi-step decision-making capabilities.
• Implement and scale multi-agent architectures to support complex, distributed use cases across payments and fleet ecosystems.
• Integrate systems using Model Context Protocol (MCP) or similar frameworks to enable secure and scalable interaction between AI agents, APIs, and enterprise data sources.
• Build and optimize LLM-powered services (e.g., OpenAI APIs, LangChain) for production-grade performance, reliability, and cost efficiency.

AI Engineer jobs in Utah

See all jobs on JobTailor

Product Engineer – AI

Lead AI Compliance Testing

AI Engineer

Enterprise AI Architect

Lead AI Engineer

Lead AI Engineer

AI & Automations Lead

AI Software Developer

Product Development Manager – AI Platform

Software Engineer 2 – AI Platform