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Mlops Jobs in Raleigh, NC (NOW HIRING)

DPR Construction

MLOps Engineer

Raleigh, NC · On-site

MLOps Engineer DPR is a leading construction company committed to delivering high-quality, innovative projects. Our team integrates cutting-edge technologies into the construction process to ...

DPR Construction

MLOps Engineer

Raleigh, NC

MLOps Engineer DPR is a leading construction company committed to delivering high-quality, innovative projects. Our team integrates cutting-edge technologies into the construction process to ...

First Citizens Bank

Principal AI Architect

Raleigh, NC · On-site

$160K - $240K/yr

AI Platform Engineering & MLOps * Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems. Architect end-to-end MLOps capabilities, including model lifecycle ...

First Citizens Bank

Principal AI Architect

Raleigh, NC

$160K - $240K/yr

AI Platform Engineering & MLOps * Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems. Architect end-to-end MLOps capabilities, including model lifecycle ...

First Citizens Bank

Principal AI Architect

Raleigh, NC

$160K - $240K/yr

AI Platform Engineering & MLOps * Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems. Architect end-to-end MLOps capabilities, including model lifecycle ...

Kastech Software Solutions Group

AI/ML LEAD

Raleigh, NC · On-site

Implement MLOps pipelines (CI/CD, monitoring, model governance). Collaborate with stakeholders to identify and prioritize AI use cases. Oversee integration of ML models into enterprise systems using ...

Berriehill Research

Senior AI Systems Engineer

Raleigh, NC · On-site +1

$92K - $126K/yr

Build and maintain CI/CD and MLOps pipelines for model packaging, testing, deployment, rollback, and lifecycle management. * Implement infrastructure automation using scripting, Infrastructure as ...

ARA

Senior AI Systems Engineer

Raleigh, NC · On-site

$92K - $126K/yr

Build and maintain CI/CD and MLOps pipelines for model packaging, testing, deployment, rollback, and lifecycle management. * Implement infrastructure automation using scripting, Infrastructure as ...

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$67K - $90K/yr

Establish and enforce MLOps best practices including model registry, artifact management, and observability. * Improve system reliability, performance, and security. * Collaborate closely with ML ...

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$67K - $90K/yr

Establish and enforce MLOps best practices including model registry, artifact management, and observability. * Improve system reliability, performance, and security. * Collaborate closely with ML ...

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$71K - $96K/yr

Establish and enforce MLOps best practices including model registry, artifact management, and observability. * Improve system reliability, performance, and security. * Collaborate closely with ML ...

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Mlops information

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$98.1K

$154K

$183.2K

How much do mlops jobs pay per year?

As of Jun 16, 2026, the average yearly pay for mlops in Raleigh, NC is $153,963.00, according to ZipRecruiter salary data. Most workers in this role earn between $145,470.00 and $167,218.00 per year, depending on experience, location, and employer.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

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 a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

Is MLOps a good career path?

MLOps is a growing field that combines machine learning, software engineering, and operations to deploy and maintain AI models efficiently. It offers high demand for skills in cloud platforms, automation, and data management, making it a promising career choice for those interested in AI infrastructure. Professionals in MLOps often work with tools like Docker, Kubernetes, and CI/CD pipelines, and typically require a strong understanding of both machine learning and software development.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What engineers make $500,000?

Senior machine learning operations (MLOps) engineers with extensive experience, specialized skills in cloud platforms, automation, and deployment often reach or exceed $500,000 annually in total compensation. High-level roles in tech companies or those with leadership responsibilities and advanced certifications tend to offer such salaries.

Which 3 jobs will survive AI?

For MLOps professionals, roles such as data scientists, machine learning engineers, and AI infrastructure engineers are expected to persist as AI adoption grows. These jobs require specialized skills in model development, deployment, and maintenance that complement automation. Continuous learning and expertise in tools like Kubernetes, cloud platforms, and version control are essential for long-term viability.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI director, often requiring advanced skills in data science, deep learning, and cloud platforms. These roles usually involve leadership, strategic planning, and extensive experience, and they may include bonuses or stock options that contribute to the total compensation. Such salaries are rare and generally found in large tech companies or specialized AI firms.

What are MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.
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Mlops jobs near you
MLOps Engineer

MLOps Engineer

DPR Construction

Raleigh, NC • On-site

Full-time

Posted 3 hours ago

DPR Construction rating

7.8

Company rating: 7.8 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

25th of 78 rated construction

Job description

Job Description
DPR is looking for an experienced Data and MLOps Engineer to join our Data and AI team and work closely with the Data Platform, BI and Enterprise architecture teams to influence the technical direction of DPR's AI initiatives.
You will work closely with cross-functional teams, including business stakeholders, data engineers, and technical leads, to ensure alignment between business needs and data architecture and define data models for specific focus areas.
MLOps Engineer
DPR is a leading construction company committed to delivering high-quality, innovative projects. Our team integrates cutting-edge technologies into the construction process to streamline operations, enhance decision-making, and drive efficiency at all levels. We are looking for a MLOps Engineer to join our team and contribute to developing robust data solutions to support our Machine Learning, Data Science, Data Engineering and Software Engineering.
Position Overview
The MLOps Engineer will be instrumental in the design and implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The MLOps Engineer will design, build and support the infrastructure and systems that enable our teams to deliver reliable, high-impact data, workflows, and collaborating closely with data engineers, software developers, data scientists and product teams.
Responsibilities
  • Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning

  • Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection

  • Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms

  • Design and deploy containerized ML workloads, partnering with Infrastructure Engineering for cluster provisioning and governance

  • Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows

  • Implement AI-driven data validation, schema drift detection, and metadata management.

  • Establish governance frameworks for AI systems, including bias detection, explainability, and auditability

  • Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention

  • Collaborate with Infrastructure Engineering to automate infrastructure provisioning

  • Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption

Qualifications
  • Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field

  • 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering

  • Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure

  • Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn

Required Skills
  • Experience with CI/CD processes and automation

  • Experience with Infrastructure as Code tools such as Terraform, Bicep

  • Proficiency in containerized application deployments and container orchestration - experience with Kubernetes, especially AKS would be a huge plus

  • Experience standing up and managing observability tools such as Datadog, Azure Monitor or Grafana for APM, LLM Ops and model performance monitoring

  • Experience deploying production-ready machine learning models

  • Experience with Model explainability (SHAP, LIME) or similar

  • Experience with cloud cost management and practices (e.g., Azure Cost Management, chargeback/show back models).

Nice to Have
  • Experience in Azure, particularly AKS, ACR, ARM, App Service, Azure Machine Learning and AI Foundry, Azure Monitor

  • Familiarity with semantic search, retrieval-augmented generation (RAG), or embedding pipelines

  • Exposure to managing and monitoring ML workloads that support generative AI or advanced analytics use cases

  • Proficiency with Snowflake

  • Experience with workflow orchestration platforms such as Apache Airflow, Argo Workflow, Prefect, etc.

DPR Construction is a forward-thinking, self-performing general contractor specializing in technically complex and sustainable projects for the advanced technology, life sciences, healthcare, higher education and commercial markets. Founded in 1990, DPR is a great story of entrepreneurial success as a private, employee-owned company that has grown into a multi-billion-dollar family of companies with offices around the world.
Working at DPR, you'll have the chance to try new things, explore unique paths and shape your future. Here, we build opportunity together-by harnessing our talents, enabling curiosity and pursuing our collective ambition to make the best ideas happen. We are proud to be recognized as a great place to work by our talented teammates and leading news organizations like U.S. News and World Report, Forbes, Fast Company and Newsweek.
Explore our open opportunities at www.dpr.com/careers.

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