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Senior Machine Learning Ops Engineer Jobs in North Carolina

CoVar

Machine Learning Engineer

Durham, NC · On-site

The Machine Learning Engineer will develop software and machine learning algorithms to address real-world customer issues and will have opportunities to present their work to high-level customers.

CoVar

Machine Learning Engineer

Durham, NC

Machine Learning Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC, that uses artificial intelligence to solve problems that matter. We develop AI/ML tools to help the ...

CoVar

Machine Learning Engineer

Durham, NC · On-site

... machine learning, Bayesian models, etc. • B.S., preferably M.S. or Ph.D in engineering, math, computer science, or related field • Excellent technical communication skills • Ability to work in ...

CRC Group

Machine Learning Engineer

Charlotte, NC · On-site

$140 - $180/hr

## Machine Learning EngineerApplylocations: Charlotte NC - 600 S Tryon St.: Morrisville NC, 3015 ... Leverage continuous engineering practices to deliver business value regarding effectiveness of the ...

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Senior Machine Learning Ops Engineer information

What are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by senior machine learning ops engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What is a senior machine learning ops engineer?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What are the most commonly searched types of Machine Learning Ops Engineer jobs in North Carolina?

The most popular types of Machine Learning Ops Engineer jobs in North Carolina are:

What are popular job titles related to Senior Machine Learning Ops Engineer jobs in North Carolina?

For Senior Machine Learning Ops Engineer jobs in North Carolina, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning Ops Engineer jobs in North Carolina look for?

The top searched job categories for Senior Machine Learning Ops Engineer jobs in North Carolina are:

What cities in North Carolina are hiring for Senior Machine Learning Ops Engineer jobs?

Cities in North Carolina with the most Senior Machine Learning Ops Engineer job openings:

Senior Machine Learning Ops Engineer jobs near you

Lead Machine Learning Engineer

Motion Recruitment Partners, LLC

Raleigh, NC • On-site

$99K - $131K/yr

Other

Posted 4 days ago

Job description

Our Client serves customers in over 150 countries with trusted legal, regulatory, and business information. They are transforming the legal industry through cutting-edge AI, scalable data platforms, and intelligent research systems that power high-stakes decision-making for legal professionals worldwide. Their Global AI Platform Team builds the foundational infrastructure behind next-generation AI products, including LLM-powered research assistants, retrieval systems, and enterprise-grade agentic workflows.
We are seeking a Sr. Machine Learning Engineer to define and lead the architecture of scalable AI/ML and agentic systems across our global product portfolio. This is a senior technical leadership role for someone who thrives at the intersection of:
  • Large-scale distributed ML systems
  • LLM and RAG architectures
  • Agentic AI frameworks and tool orchestration
  • Enterprise platform engineering

You will shape the long-term AI platform strategy and establish technical standards that impact millions of users
What You'll
  • Do Architect Scalable AI Platforms
  • Define reference architecture for LLM, ML, and agent-based systems across products
  • Design high-availability, low-latency inference platforms for global scale
  • le.Establish reusable platform components for model lifecycle, deployment, and monitori
  • Lead Agentic AI & Tool Ecosystems
  • Architect multi-step, reasoning-driven agent systems
  • Design orchestration patterns for tool use, API invocation, and structured function calling
  • Lead implementation and governance of Model Context Protocol (MCP) servers to standardize tool integration and context management
  • Define guardrails, permissions, and audit mechanisms for enterprise-safe AI systems
  • Elevate Engineering Standards. Set best practices for MLOps, CI/CD, observability, and system reliability
  • Embed Responsible AI principles across platform architecture
  • Mentor senior engineers and influence technical direction across teams

What We're Looking
For Experience/Education requirement
  • 10+ yrs of experience with Master's degree or 12+ yrs of experience with bachelor degree
  • 10+ years building production-grade ML systems at scale
  • Extensive experience with LLMs, generative AI, and RAG systems in real-world deployments
  • Proven expertise designing distributed systems in cloud environments (AWS, Azure, or Google Cloud Platform)
  • Hands-on experience with Kubernetes, containerization, and scalable inference systems
  • Experience designing agentic systems and tool orchestration frameworks
  • Experience implementing or governing MCP servers or structured tool-calling architectures
  • Technical Strength/Strong Python engineering background
  • Experience with vector databases and search systems
  • Deep understanding of model evaluation, reliability, and monitoring
  • Strong architectural judgment and systems thinking
  • Leadership/Demonstrated ability to influence technical direction across teams
  • Strong communication skills and executive presence
  • Experience mentoring senior engineers or leading cross-functional initiatives

Apply