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Mlops Data Engineer Jobs in Raleigh, NC (NOW HIRING)

Architect, Data AI

Durham, NC · On-site

$61.50 - $79.25/hr

... and collaborate with product and engineering teams to deliver measurable AI outcomes ... Background in MLOps/CI-CD pipelines for deploying and monitoring ML models at scale. • ...

Architect, Data AI

Durham, NC

$61.50 - $79.25/hr

... engineering, and partner directly with product, engineering, and customer-facing leaders to ... Background in MLOps/CI-CD pipelines for deploying and monitoring ML models at scale. • ...

Architect, Data AI

Durham, NC

$61.50 - $79.25/hr

Partner with business, product, and engineering leaders to translate procurement and supply chain ... Background in MLOps/CI-CD pipelines for deploying and monitoring ML models at scale. Familiarity ...

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Model Deployment and MLOps : Oversee the deployment of machine learning models into production ... Proficiency in multiple programming languages relevant to data science (Python, R, etc.) and big ...

Model Deployment and MLOps : Oversee the deployment of machine learning models into production ... Proficiency in multiple programming languages relevant to data science (Python, R, etc.) and big ...

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Showing results 1-20

Mlops Data Engineer information

See Raleigh, NC salary details

$43.3K

$126.1K

$172.5K

How much do mlops data engineer jobs pay per year?

As of Jul 17, 2026, the average yearly pay for mlops data engineer in Raleigh, NC is $126,095.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,300.00 and $133,700.00 per year, depending on experience, location, and employer.

What is the difference between Mlops Data Engineer vs Data Scientist?

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps engineers in demand?

MLOps Data 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 as organizations prioritize operationalizing AI solutions.

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

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps Data Engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What are MLOps Data Engineers?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What is the salary of data engineer in MLOps?

The salary of an MLOps Data Engineer typically ranges from $90,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning tools tend to earn higher salaries.

What engineer makes 500,000 a year?

Highly experienced senior MLOps Data Engineers with specialized skills in cloud platforms, automation, and large-scale data processing can earn salaries approaching or exceeding $500,000 annually, especially in competitive tech hubs or large organizations. Such roles often require advanced certifications, extensive experience, and expertise in tools like Kubernetes, Docker, and cloud services like AWS or Azure.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer’s ability to support scalable and reliable ML systems.
What cities near Raleigh, NC are hiring for Mlops Data Engineer jobs? Cities near Raleigh, NC with the most Mlops Data Engineer job openings:
Senior Software Engineer (Pipeline team)

Senior Software Engineer (Pipeline team)

Foundation AI

Raleigh, NC • On-site

$119K - $157K/yr

Full-time

Re-posted 11 days ago


Job description

About Us

Foundation AI is the only AI Native document intake automation platform serving the claims and litigation industries. Founded in 2019 by a team of lawyers and data scientists, Foundation AI processes millions of documents each month for hundreds of US law firms, including many of the largest and most respected plaintiff and injury law firms in the country.

Job Overview

We're looking for a Senior AI/ML Engineer to help expand our next-generation document intelligence system. Working in close collaboration with our Data Science team, you'll bring deep technical rigor to a system that gets smarter with every document it digests, across hundreds of customers at scale. The system draws on a combination of ML, LLM, RAG, applied mathematics, and smart algorithm design to deliver results at a high level of accuracy.
This is a remote job.

Key Responsibilities
  • Retrieval-Augmented Generation: Design and build RAG architectures for document understanding, classification, and extraction — from chunking and indexing through retrieval quality and grounding.
  • LLM Feature Development: Ship production LLM-powered features end-to-end, from prompt design through evaluation — not just prototypes.
  • Evaluation-Driven Development: Build regression suites, confidence calibration methods, and evaluation frameworks that make AI output quality measurable.
  • Collaboration with Data Science: Partner closely with our Data Science team to bring research-grade techniques into production.
  • ML Pipeline & MLOps: Own model, data, and prompt versioning; build reproducible pipelines for ingestion, training, evaluation, and serving.
  • Rollout Automation & A/B Testing: Implement canary deployments, side-by-side A/B testing, and rollback mechanisms for safe model and prompt releases.
  • Monitoring & Observability: Implement drift detection, data quality monitoring, and alerting; define SLOs for model and pipeline health.
  • System Architecture & Leadership: Design secure, high-performance ML infrastructure; evaluate tooling (Bedrock, MLflow, Airflow); mentor engineers and influence best practices.
Skills and Tools
  • Experience: 5+ years in software engineering, with 2–3 years focused on ML/AI in production systems.
  • LLM & RAG Fundamentals: Hands-on experience with prompt engineering, RAG architectures, and evaluation-driven development — with a track record of shipping LLM-powered features real users rely on.
  • MLOps & Pipeline Tooling: Practical experience with model/data/prompt versioning, experiment tracking, and deployment automation; proficiency with Airflow, MLflow, and Bedrock or equivalents.
  • Programming: Proficient in Python; comfortable with SQL and data engineering patterns.
  • Strongly Preferred: Working understanding of classical ML methods (gradient boosting, embeddings, calibration) sufficient to collaborate closely with Data Science; AWS infrastructure experience (S3, ECS/EKS, Lambda); familiarity with agent frameworks (LangChain, MCP) is a bonus.
Education

A B.Tech degree in Computer Science or equivalent experience relevant to the functional area.

Why You 'll Love Working With Us You'll Love Working With Us
We believe great work happens when people feel valued, heard, and supported — and we're proud of the culture we've built together, no matter where in the world our team calls home.

This year, we were recognized as #49 on Forbes' list of America's Best Startup Employers — a reflection of the environment we've worked hard to create and the people who make it thrive.

But recognition from the outside only tells part of the story. What matters most to us is what our own team says. In our latest internal Gallup engagement survey, our employees gave us strong marks across the board — and we don't just celebrate those results, we act on them. Employee feedback directly shapes how we improve our workplace, from the way we support wellbeing to how we grow careers and build community across our global team.

When you join us, you're joining a company that listens, adapts, and invests in getting better — because we know our people are the reason we succeed.
Our Commitment

Foundation AI is an equal opportunity employer committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic. Our hiring decisions are based solely on qualifications, merit, and business needs at the time.

For any feedback or inquiries, please contact us at careers@foundationai.com.
Learn more at www.foundationai.com.

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