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Director Embedded Ai Jobs in Utah (NOW HIRING)

LVT

Senior AI Specialist

American Fork, UT

Audit and rationalize the AI capabilities embedded across LVT's existing tech stack - driving ... without direct authority over every team involved. * Data-Environment Comfortable: You're ...

LVT

Senior AI Specialist

American Fork, UT · On-site

Audit and rationalize the AI capabilities embedded across LVT's existing tech stack -- driving ... without direct authority over every team involved. * Data-Environment Comfortable: You're ...

LVT

Senior AI Specialist

American Fork, UT · On-site

Audit and rationalize the AI capabilities embedded across LVT's existing tech stack - driving ... without direct authority over every team involved. * Data-Environment Comfortable: You're ...

Doist

Director of Engineering

Salt Lake City, UT · On-site

$180 - $240/hr

... or an embedded platform experience serves the customer best * Measure success by effort removed and time saved for the customer, not features shipped * Direct AI-Native Development * Architect ...

CardWorks

Director, Data Science

South Jordan, UT · On-site

We're embedded throughout the credit card ecosystem as a lender, servicer, and merchant acquirer ... The Director, Data Science - Competitive Intelligence, AI Insights & Strategic Analytics is a ...

Acara Solutions

Software Engineering

Salt Lake City, UT · On-site

$60 - $65/hr

Develop embedded software from requirements and design through implementation, integration, formal ... AI agent role is to help speed up your hiring process by answering questions, confirming basic ...

New

Verisk

UX Director

Lehi, UT · On-site

$125K - $150K/yr

Direct UX research strategy, ensuring evidence is embedded in major product and design decisions * Establish and scale design systems, UX standards, and accessibility practices * Define where AI and ...

iblades.ai

Be Seen First

Electronics Technician

Ogden, UT · On-site

$68K - $80K/yr

Our products combine custom electronics, embedded systems, and advanced software to deliver new ... Direct collaboration with engineers building cutting-edge cybersecurity hardware * Opportunity to ...

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Director Embedded Ai information

What does a director of embedded AI do?

A Director of Embedded AI oversees the development and integration of artificial intelligence technologies into hardware devices and embedded systems. They lead teams of engineers and data scientists to create smart, efficient, and secure AI solutions for products such as IoT devices, automotive systems, medical devices, and more. This role involves setting strategic direction, managing projects, ensuring compliance with industry standards, and collaborating with other departments to bring AI-driven features to market. Directors of Embedded AI also stay up-to-date with advancements in AI and embedded systems to maintain a competitive edge.

What are the key skills and qualifications needed to thrive as a director of embedded AI?

To thrive as a Director of Embedded AI, you need deep expertise in embedded systems, AI/machine learning algorithms, and a relevant advanced degree such as a master's or PhD in computer science or engineering. Familiarity with tools like TensorFlow Lite, PyTorch, edge AI platforms, and experience managing cross-functional development teams are crucial, along with certifications in AI or embedded systems being advantageous. Leadership, strategic vision, and excellent communication skills are essential for guiding teams and aligning projects with organizational goals. These capabilities ensure the successful integration of AI solutions into embedded products, driving innovation and competitive advantage.

How does a director of embedded AI typically collaborate with cross-functional teams to drive project success?

A Director of Embedded AI regularly works alongside hardware engineers, software developers, product managers, and data scientists to ensure AI solutions are seamlessly integrated into embedded systems. Collaboration involves aligning AI capabilities with hardware constraints, overseeing the development pipeline, and facilitating communication between teams to address technical challenges. This role often leads strategic planning sessions, reviews project milestones, and ensures all stakeholders are informed about progress and requirements. Effective collaboration is crucial for delivering innovative products on time and within budget.

What are the most commonly searched types of Embedded Ai jobs in Utah?

The most popular types of Embedded Ai jobs in Utah are:

What cities in Utah are hiring for Director Embedded Ai jobs?

Cities in Utah with the most Director Embedded Ai job openings:

Director Embedded Ai jobs near you

Senior AI Specialist

LVT

American Fork, UT

Full-time

Re-posted 26 days ago

Job description

ABOUT THIS ROLE

LVT is looking for a hands-on AI practitioner to own and execute our AI initiatives across the organization. As an AI Specialist, you will build and ship production AI systems, shape how LVT adopts AI at scale, and partner cross-functionally to turn AI from an experiment into a competitive advantage. This is an individual contributor role reporting into IT leadership - you'll work directly with Data Engineering and the Automation Integrations team to build RAG pipelines, develop AI policy, rationalize our AI tool investments, and embed intelligent components within our automation workflows.

This role is based in-office out of our Headquarters in American Fork, Utah.

ROLE RESPONSIBILITIES

  • Execute LVT's AI adoption strategy, owning the roadmap of AI initiatives and driving measurable business value across departments.

  • Develop and own LVT's internal AI policy framework, setting standards for responsible AI use, governance, and organizational readiness.

  • Audit and rationalize the AI capabilities embedded across LVT's existing tech stack - driving adoption of high-value tools, consolidating redundancies, and deprecating underutilized spend.

  • Build and deploy RAG pipelines and Cortex-based models in partnership with Data Engineering, translating business needs into production-ready AI solutions.

  • Architect the AI strategy at the intersection of automation - defining where and how AI augments workflows built on platforms like Workato and Oracle Integration Cloud (OIC).

  • Design and build the AI components embedded within automation flows, partnering with the Automation Integrations team to ensure seamless, intelligent execution.

  • Champion AI literacy and adoption across LVT, partnering with business leaders to identify high-impact opportunities and drive measurable outcomes.

  • Communicate AI strategy and progress to executive leadership, translating technical direction into clear business outcomes.

OUR IDEAL CANDIDATE

  • AI Practitioner First: You have built and shipped LLM-based systems in production - not just used AI-powered SaaS tools. Strategy matters, but your hands are in the code.

  • Proven AI Depth: You have substantive, hands-on experience building and deploying LLM-based solutions - RAG pipelines, vector databases, agent frameworks, prompt engineering at scale. Using Copilot or ChatGPT at work does not qualify. We need someone who can build the systems, not just use them.

  • Platform Fluent: You understand enterprise automation platforms (Workato, OIC, or equivalent) and can reason clearly about where AI fits within complex integration flows.

  • Tech Stack Evaluator: You know how to assess embedded AI features across SaaS and enterprise tools - cutting through vendor marketing to determine what's actually delivering value, what's redundant, and what should go.

  • Policy-Minded: You've developed or contributed to AI governance frameworks, usage policies, or organizational AI standards - and you understand the stakes of getting it right.

  • Cross-Functional Influencer: You build trust with engineering, operations, and executive stakeholders alike, and know how to move initiatives forward without direct authority over every team involved.

  • Data-Environment Comfortable: You're experienced working within data platforms (e.g., Snowflake, Cortex) and partnering with data engineering teams to bring AI models from concept to production.

  • Outcome-Oriented: You measure your work by business impact - adoption rates, efficiency gains, cost reduction - not by the sophistication of the model you shipped.

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