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Mlops Engineer Jobs in Raleigh, NC (NOW HIRING)

Deloitte

AI Engineer Consultant

Raleigh, NC · Hybrid

... LLMOps/MLOps capabilities (evaluation, monitoring, governance workflows, model/prompt/version ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

Vadum, Inc.

AI Solutions Engineer

Raleigh, NC · On-site

$121K - $173K/yr

Familiarity with MLOps workflows, CI/CD, containerization (Docker/Kubernetes). * Background in enterprise data systems or API-first architectures. * Previous technical consulting or field engineering ...

Vadum Inc

AI Solutions Engineer

Raleigh, NC · On-site

Familiarity with MLOps workflows, CI/CD, containerization (Docker/Kubernetes). * Background in enterprise data systems or API-first architectures. * Previous technical consulting or field engineering ...

Vadum Inc

AI Solutions Engineer

Raleigh, NC · On-site

Familiarity with MLOps workflows, CI/CD, containerization (Docker/Kubernetes). * Background in enterprise data systems or API-first architectures. * Previous technical consulting or field engineering ...

Deloitte

Lead Forward Deployed Engineer - AWS

Raleigh, NC · On-site

$99K - $131K/yr

Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt ... Solution Engineering * Build AI-enabled solutions, agentic platforms, and workflows across ...

Deloitte

Senior Forward Deployed Engineer- AWS

Raleigh, NC · On-site

$101K - $139K/yr

Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt ... Solution Engineering * Build AI-enabled solutions, agentic platforms, and workflows across ...

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Mlops Engineer information

Are MLOps engineers in demand?

MLOps engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

What is an MLOps engineer?

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and operations by automating workflows, optimizing infrastructure, and ensuring model reliability. Their role includes CI/CD for ML models, data pipeline management, and performance monitoring. They also work with cloud platforms, containerization, and orchestration tools to scale ML systems efficiently.

What are some common challenges MLOps engineers face in their daily work?

Mlops Engineers often encounter challenges in integrating new machine learning models into existing production systems while ensuring minimal downtime and maintaining data integrity. Managing the scaling and orchestration of models across various cloud or on-prem environments can be complex, requiring close coordination with data scientists and DevOps teams. Staying up to date with rapidly evolving tools and best practices is also essential in this field. Addressing these challenges provides valuable opportunities to innovate and improve both technical processes and team collaboration.

What are the key skills and qualifications needed to thrive as an MLOps engineer, and why are they important?

To thrive as an Mlops Engineer, you need strong skills in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science, engineering, or a related field. Familiarity with tools such as Docker, Kubernetes, TensorFlow, AWS/GCP/Azure, and CI/CD systems is essential, and certifications like AWS Certified Machine Learning or Kubernetes Administrator are often valued. Effective communication, problem-solving, and teamwork are crucial soft skills for collaborating across data science and IT teams. These abilities enable Mlops Engineers to efficiently deploy, manage, and scale machine learning models in dynamic production environments.

What does an MLOps engineer do?

An MLOps engineer is responsible for deploying, managing, and maintaining machine learning models in production environments. They work with tools like Docker, Kubernetes, and cloud platforms to automate workflows, ensure model reliability, and monitor performance. Their role combines software engineering, data science, and DevOps practices to streamline the deployment and lifecycle management of machine learning systems.
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What cities near Raleigh, NC are hiring for Mlops Engineer jobs? Cities near Raleigh, NC with the most Mlops Engineer job openings:
Mlops Engineer jobs near you
Infographic showing various Mlops Engineer job openings in Raleigh, NC as of August 2026, with employment types broken down into 87% Full Time, 7% Part Time, and 6% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

Staff AI Engineer

Western Governors University

Raleigh, NC • On-site

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 26 days ago

Western Governors University rating

8.5

Company rating: 8.5 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

80th of 617 rated colleges and universities

Job description

If you're passionate about building a better future for individuals, communities, and our country-and you're committed to working hard to play your part in building that future-consider WGU as the next step in your career.

Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.

The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:

Grade: Technical 412

Job Description

As a Staff AI Engineer, you will serve as a technical leader and force multiplier within the AI Engineering Enablement team. You will define how AI systems are architected, deployed, and scaled across the organization. Operating at the intersection of deep technical expertise and organizational leadership, you will shape platform strategy, influence roadmaps, and elevate engineering capabilities across multiple teams.
What You'll Do
  • Define and own AI engineering architecture standards, design patterns, and platform conventions for LLM-based systems
  • Lead complex, cross-functional AI initiatives from inception through delivery, aligning engineering, product, data science, and research stakeholders
  • Drive build-vs-buy and vendor evaluation decisions for AI frameworks, models, and infrastructure
  • Design and scale internal AI platforms including shared tooling, reusable components, prompt libraries, and evaluation infrastructure
  • Establish and mature LLMOps practices including governance, cost management, observability, and safe deployment standards
  • Lead AI safety initiatives including red-teaming, adversarial testing, and responsible AI policy development
  • Mentor and develop Senior and II-level engineers through coaching, design reviews, and technical leadership
What You'll Bring
  • Expert-level, production-proven experience across the AI engineering stack including LLM APIs, agentic systems, RAG pipelines, evaluation frameworks, and LLMOps
  • Demonstrated ability to define and drive architectural patterns and engineering standards at team or organizational scale
  • Deep expertise in agentic system design including multi-agent architectures, state management, and reliability engineering for non-deterministic systems
  • Strong platform engineering experience designing shared infrastructure, reusable tooling, and developer-facing systems
  • Advanced knowledge of LLM fine-tuning, alignment techniques, and evaluation methodologies including safety and bias assessment
  • Experience leading vendor evaluations and technical due diligence for AI frameworks and infrastructure
  • Strong proficiency in Python and software engineering fundamentals with a focus on quality, testing, and reliability standards
  • Bachelor's Degree in Computer Science, Software Engineering, Data Science, Machine Learning, Math, or a related field. Master's Degree strongly preferred.
  • 7+ years of experience in software engineering, data science, or machine learning
  • 5+ years of hands-on experience building and deploying LLM-based or AI systems in production at scale
  • Demonstrated experience setting architectural direction across teams or organizations
  • Experience leading complex AI projects across multiple teams or functional areas
  • Proven mentorship of Senior and/or II-level engineers
  • Experience designing and operating shared AI platforms or internal AI infrastructure
  • Experience owning LLMOps or MLOps practices including governance, rollout strategy, and production monitoring
  • Experience with AWS cloud architectures including scalable inference, data pipelines, and cost optimization
  • Hands-on experience with fine-tuning, PEFT, and model evaluation in production environments
Bonus Points
  • Master's Degree or PhD in Computer Science, AI/ML, or a related field
  • Experience with Databricks and related certifications
  • AWS certifications such as AWS Certified Machine Learning - Specialty
  • Experience with open-source model ecosystems and self-hosted inference infrastructure
  • Experience in EdTech, personalized learning, or student-facing AI platforms
  • Published research, conference presentations, or open-source contributions in AI/ML
  • Experience with enterprise AI governance, compliance frameworks, or regulatory requirements
Experience in Lieu of Education
Equivalent relevant experience performing the essential functions of this job may substitute for graduate education degree preferences or requirements.
What to Expect
At WGU, our mission drives everything we do, including how we hire. Our interview experience is designed to give qualified candidates the opportunity to show their best work through meaningful conversations and collaboration.
We thoughtfully review every application and invite forward the candidates whose experience and potential best align with the role and our mission.
  • Introductory call
  • Hiring manager interview
  • Technical interview
  • Final panel interview
Work Location
This is a full-time, in-office position requiring five days per week in our Raleigh, NC office, designed to foster the collaboration and connection that fuel our best work.
Visa Sponsorship
While we welcome applicants from all backgrounds, WGU is not able to provide visa sponsorship for this role.
Travel Requirement
This position requires occasional travel of up to 20%, including required attendance at designated company summits (typically one to two per year). Additional travel may include conferences, visits to company locations, and other business-related events as needed. Additional travel may be assigned as needed to support business requirements.

#LI-AW2

Position & Application Details

Full-Time Regular Positions (classified as regular and working 40 standard weekly hours): This is a full-time, regular position (classified for 40 standard weekly hours) that is eligible for bonuses; medical, dental, vision, telehealth and mental healthcare; health savings account and flexible spending account; basic and voluntary life insurance; disability coverage; accident, critical illness and hospital indemnity supplemental coverages; legal and identity theft coverage; retirement savings plan; wellbeing program; discounted WGU tuition; and flexible paid time off for rest and relaxation with no need for accrual, flexible paid sick time with no need for accrual, 11 paid holidays, and other paid leaves, including up to 12 weeks of parental leave.

How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.

Additional Information

Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It's not all-inclusive.

Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at recruiting@wgu.edu.

Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to any protected characteristic as required by law.

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