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Ai Platform Engineer Jobs in Draper, UT (NOW HIRING)

OC Tanner

Sr. Applied AI Engineer

Salt Lake City, UT · On-site

$101K - $138K/yr

About the Role AI is becoming part of the product and platform architecture we need to build, operate, and scale. We are looking for an Applied AI Engineer who can turn AI capability into secure ...

Epicor

Manager of Product Development | AI Platform

Lehi, UT · Hybrid

$107K - $134K/yr

Leading, mentoring, and growing a global team of software engineers and Forward Deployed Engineers ... platforms * Hands-on experience with Azure AI Services, Azure OpenAI, Azure Kubernetes Service ...

Wsgr

AI Systems Manager

Salt Lake City, UT · On-site

The ASM oversees the full range of firm-wide AI tools, including chat-based, agentic and developer-facing products, and is accountable for ensuring these platforms remain secure, well-governed, cost ...

KēSTA I.T.

Principal DevOps Engineer

Salt Lake City, UT · On-site

$50.75 - $69.50/hr

Architect cloud and AI platform strategies and clearly communicate proposed solutions to both technical and non-technical stakeholders * Provide technical leadership to engineering team members ...

Pattern

Staff Data Platform Engineer

Lehi, UT · On-site

$130 - $160/hr

Utilizing more than 66 trillion data points, sophisticated machine learning and AI models, Pattern ... We are looking for a visionary Staff Data Platform Engineer to lead the design, architecture, and ...

Pattern

Staff Data Platform Engineer

Lehi, UT · On-site

Utilizing more than 66 trillion data points, sophisticated machine learning and AI models, Pattern ... We are looking for a visionary Staff Data Platform Engineer to lead the design, architecture, and ...

Pattern

Staff Data Platform Engineer

Lehi, UT

Utilizing more than 66 trillion data points, sophisticated machine learning and AI models, Pattern ... We are looking for a visionary Staff Data Platform Engineer to lead the design, architecture, and ...

Pattern

Staff Data Platform Engineer

Lehi, UT

Utilizing more than 66 trillion data points, sophisticated machine learning and AI models, Pattern ... We are looking for a visionary Staff Data Platform Engineer to lead the design, architecture, and ...

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Ai Platform Engineer information

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How much do ai platform engineer jobs pay per hour?

As of Aug 30, 2026, the average hourly pay for ai platform engineer in Draper, UT is $59.79, according to ZipRecruiter salary data. Most workers in this role earn between $47.21 and $68.99 per hour, depending on experience, location, and employer.

What is an AI Platform Engineer?

AI Platform Engineers are technology professionals who design, build, and maintain the infrastructure that supports the development, deployment, and scaling of artificial intelligence (AI) and machine learning (ML) models. They work closely with data scientists and software engineers to ensure that AI solutions can run efficiently and securely in production environments. Their responsibilities often include managing cloud or on-premises platforms, automating workflows, and implementing best practices for model versioning, monitoring, and resource optimization.

How does an AI Platform Engineer typically collaborate with data scientists and software engineers in a project environment?

AI Platform Engineers often serve as a bridge between data scientists and software engineers, ensuring that machine learning models are seamlessly integrated into scalable, production-ready systems. They work closely with data scientists to understand model requirements and deployment needs, and with software engineers to embed these models within applications and services. This collaboration involves frequent communication, joint troubleshooting, and participation in code reviews to maintain a robust and efficient AI infrastructure.

What are the key skills and qualifications needed to thrive as an AI Platform Engineer, and why are they important?

To thrive as an AI Platform Engineer, you need strong programming skills (especially in Python and Java), a background in computer science or related fields, and experience with machine learning frameworks. Familiarity with cloud platforms (like AWS, Azure, or GCP), containerization tools (Docker, Kubernetes), and CI/CD systems is typically required, along with certifications such as Google Cloud Professional Machine Learning Engineer. Excellent problem-solving, collaboration, and communication skills help you integrate AI solutions across teams and projects. These competencies ensure the efficient development, deployment, and maintenance of scalable AI systems in dynamic production environments.

What is the difference between Ai Platform Engineer vs Data Engineer?

AspectAi Platform EngineerData Engineer
CredentialsBachelor's in CS, AI, or related; experience with cloud platformsBachelor's in CS, Data Science, or related; experience with databases and ETL tools
Work EnvironmentDeveloping AI infrastructure, deploying ML models, working with cloud servicesBuilding data pipelines, managing data storage, ensuring data quality
Industry UsageTech companies, AI startups, cloud providersFinance, healthcare, e-commerce, any data-driven industry

While both roles involve working with data and cloud platforms, Ai Platform Engineers focus on building and maintaining AI infrastructure and deploying machine learning models. Data Engineers primarily develop data pipelines and manage data storage. The roles often collaborate but serve different core functions within AI and data ecosystems.

How to become an AI platform engineer?

To become an AI platform engineer, you should have a strong background in computer science, software engineering, or related fields, with expertise in machine learning frameworks, cloud computing, and programming languages like Python or Java. Gaining experience with AI tools, data management, and infrastructure deployment is essential, often supported by certifications in cloud platforms such as AWS or Azure. Building a portfolio of projects and staying updated on AI and DevOps practices can also enhance your qualifications.

What does an AI platform engineer do?

An AI platform engineer designs, develops, and maintains the infrastructure and tools needed to deploy and manage artificial intelligence models at scale. They work with cloud services, programming languages, and machine learning frameworks to ensure efficient model training, deployment, and monitoring in production environments.

What is the salary of AI platform engineer?

The salary of an AI platform engineer typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and machine learning may earn higher compensation.

What are popular job titles related to Ai Platform Engineer jobs in Draper, UT?

For Ai Platform Engineer jobs in Draper, UT, the most frequently searched job titles are:

What cities near Draper, UT are hiring for Ai Platform Engineer jobs?

Cities near Draper, UT with the most Ai Platform Engineer job openings:

Ai Platform Engineer jobs near you

Sr. Applied AI Engineer

OC Tanner

Salt Lake City, UT • On-site

$101K - $138K/yr

Full-time

Re-posted 18 days ago

Job description

O.C. Tanner is the global leader in software and services that improve workplace culture through meaningful employee experiences. Our Culture Cloud is a suite of apps designed to enhance the employee experience with strategic recognition, service awards, wellbeing, leadership, and events that help people thrive at work. Our Culture by Design approach provides expert services to organizations looking to create great workplaces.
Our global team of 1,500 people hail from 58 countries and speak 62 languages. As programmers, researchers, designers, client professionals and craftspeople we create the tech, tools and awards that connect employees to purpose at thousands of companies. Join us as we help people all over the world thrive at work.
About the Role
AI is becoming part of the product and platform architecture we need to build, operate, and scale. We are looking for an Applied AI Engineer who can turn AI capability into secure, measurable, governed production systems, not prototypes or demos. This person will help define how O.C. Tanner builds agentic systems that pursue goals, use tools, follow guardrails, recover from failure, and deliver real value inside user workflows.
This role sits at the intersection of software engineering, product experience, AI platform engineering, and responsible AI. You will partner with Product, UX, Design, Architecture, Security, and Engineering to build AI experiences that are useful, understandable, reliable, and safe to operate in production. The right person has hands-on experience building agentic systems with orchestration, tool calling, memory or state, RAG, evaluation, observability, and human-in-the-loop controls.
Responsibilities
  • Design, build, deploy, and support production-grade agentic AI systems that operate against explicit goals, constraints, policies, and guardrails.
  • Build agent orchestration patterns for multi-step workflows, tool calling, MCP servers, state management, memory, retries, recovery paths, and human-in-the-loop controls.
  • Partner closely with Product, UX, Design, Architecture, Security, and Engineering teams to create AI experiences that are useful, understandable, reliable, and aligned with real user workflows.
  • Design user-centered AI interactions, including conversational flows, feedback loops, confidence handling, explainability, graceful failure modes, escalation paths, and clear boundaries for autonomous behavior.
  • Develop and operate RAG systems that ground model behavior in enterprise knowledge, including ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, retrieval evaluation, and citation or traceability strategies.
  • Define and implement evaluation frameworks for AI systems, including offline test sets, regression suites, adversarial testing, groundedness and faithfulness scoring, task completion metrics, and production quality monitoring.
  • Instrument agentic systems for observability, including traces of model calls, prompts, tool usage, decisions, retrieved context, latency, cost, errors, and user feedback.
  • Establish safeguards for responsible AI use, including prompt injection defense, data access controls, PII protection, bias and toxicity detection, misuse prevention, audit logging, and policy enforcement.
  • Optimize model selection, prompts, context windows, caching, routing, inference patterns, latency, throughput, reliability, and cost across production workloads.
  • Mentor engineers on applied AI practices, including prompt and context engineering, agent design, RAG, evaluation, safety, observability, and production support.
  • Stay current with emerging AI platforms, frameworks, models, and standards.

Our stack
  • Python / FastAPI microservices
  • LangChain / LangGraph
  • GraphQL / REST
  • PostgreSQL / Redis
  • Kafka
  • Kubernetes
  • AWS Bedrock
  • OpenTelemetry
  • Terraform

Qualifications
Required Qualifications
  • 5+ years of software engineering experience with strong Python proficiency
  • 2+ years building production ML or agentic AI systems
  • 1+ years hands-on experience with agentic frameworks (LangGraph, CrewAI, AutoGen, or equivalent)
  • Built production AI systems including agents, MCP servers, multi-step reasoning, and multi-turn conversation
  • Deployed RAG systems including embedding models, vector databases, hybrid search, and retrieval optimization
  • Designed LLM strategies covering tool calling, structured outputs, prompt engineering, and context window management
  • Implemented AI safety and evaluation pipelines covering bias detection, PII leakage, faithfulness scoring, toxicity, and prompt injection mitigation
  • Optimized models for inference efficiency, latency, and cost management

Strongly Preferred
  • Bachelor's degree in Computer Science, Machine Learning, or a related field
  • AWS Certified Machine Learning Engineer - Associate or equivalent
  • Cloud AI infrastructure management using AWS services and Terraform
  • AI observability experience with OpenTelemetry, Langfuse, or equivalent

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