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Mlops Data Engineer Jobs in Rochester, MI (NOW HIRING)

Deloitte

Forward Deployed Engineer- AWS

Detroit, MI · On-site

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 ...

Stellantis

Data Scientist

Auburn Hills, MI · On-site

Data scientists work closely with data engineers, analysts, and business teams to design analytics ... Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines

Reply

Manager, Data Engineering

Detroit, MI · On-site

$160K - $190K/yr

Ability to create working environments for data engineers and scientists, and general knowledge of ML, AI, LLMs, and MLOps * Knowledge and familiarity with Microsoft Purview * DevOps for data, GitHub ...

Reply

Manager, Data Engineering

Detroit, MI

$160K - $190K/yr

Ability to create working environments for data engineers and scientists, and general knowledge of ML, AI, LLMs, and MLOps * Knowledge and familiarity with Microsoft Purview * DevOps for data, GitHub ...

Reply

Manager, Data Engineering

Detroit, MI · On-site

$160K - $190K/yr

Ability to create working environments for data engineers and scientists, and general knowledge of ML, AI, LLMs, and MLOps * Knowledge and familiarity with Microsoft Purview * DevOps for data, GitHub ...

General Motors

Staff Data Scientist

Warren, MI

Production ML and MLOps * Package and deploy models as reliable production services, batch processes, or decision-support capabilities in partnership with software, data, and platform engineers.

General Motors

Staff Data Scientist

Warren, MI · On-site

Production ML and MLOps * Package and deploy models as reliable production services, batch processes, or decision-support capabilities in partnership with software, data, and platform engineers.

Conclusive Marketing

Data Scientist

Detroit, MI

Collaborate with engineering teams to implement MLOps practices including model deployment, monitoring, and end-to-end lifecycle management using tools such as MLflow * Adhere to data governance ...

Ilitch

Applied AI Engineer

Detroit, MI · On-site

... data architectures and how storage, compute, and governance choices influence AI solution design * Understanding of modern product and engineering practices such as Agile, DevOps, CI/CD,MLOps, and ...

General Motors

AI/ML Data Scientist (GPSSC)

Warren, MI · On-site

This role requires programming and statistical techniques to solve complex problems and work within ... Deployment & MLOps: MLflow , Model Monitoring & Versioning, Docker & Kubernetes, GitHub , Jira

General Motors

AI/ML Data Scientist (GPSSC)

Warren, MI · On-site

This role requires programming and statistical techniques to solve complex problems and work within ... Deployment & MLOps: MLflow , Model Monitoring & Versioning, Docker & Kubernetes, GitHub , Jira

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Showing results 21-40

All JobsMlops Data Engineer JobsMlops Data Engineer Jobs in Rochester, MI

Mlops Data Engineer information

See Rochester, MI salary details

$41K

$119.4K

$163.4K

How much do mlops data engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for mlops data engineer in Rochester, MI is $119,398.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,400.00 and $126,600.00 per year, depending on experience, location, and employer.

What is an MLOps data engineer?

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 are the key skills and qualifications needed to thrive as an MLOps data engineer?

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 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 Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining ML models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

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 job categories do people searching Mlops Data Engineer jobs in Rochester, MI look for?

The top searched job categories for Mlops Data Engineer jobs in Rochester, MI are:

What cities near Rochester, MI are hiring for Mlops Data Engineer jobs?

Cities near Rochester, MI with the most Mlops Data Engineer job openings:

Mlops Data Engineer jobs near you
Infographic showing various Mlops Data Engineer job openings in Rochester, MI as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $119,398 per year, or $57.4 per hour.

Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV

Dana Incorporated

Novi, MI • On-site

Full-time

Re-posted 5 days ago

Dana Incorporated rating

5.9

Company rating: 5.9 out of 10

Based on 79 frontline employees who took The Breakroom Quiz

453rd of 494 rated machine equipment manufacturers

Job description

Job Purpose
The Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV is a senior leadership role responsible for defining and executing the company's enterprise-wide data strategy, with a strong focus on Master Data Management (MDM), data governance, and AI-driven transformation across Aftermarket (AFM) and Commercial domains.
This role leads the end-to-end data value chain-from master data integrity and governance to advanced analytics and AI-ensuring that enterprise data is trusted, unified, and actionable. The Sr. Director will partner closely with business, digital, and engineering leaders to embed data and AI into core commercial and operational processes, driving measurable outcomes in revenue growth, customer experience, and operational performance.
Job Duties and Responsibilities
Enterprise Data & AI Strategy Leadership
• Define and lead the enterprise data strategy, anchored in MDM, data governance, analytics, and AI, aligned to AFM and Commercial growth priorities.
• Establish a multi-year roadmap spanning master data, data platforms, analytics, and AI/GenAI capabilities.
• Act as a strategic advisor to executive leadership, shaping how data and AI drive competitive advantage, revenue, and operational excellence.
• Build and lead a high-performing global organization across MDM, data engineering, governance, analytics, and data science.
Master Data Management (MDM) & Data Governance
• Own and institutionalize enterprise MDM strategy and platforms across core domains (Customer, Product, Supplier, Pricing, Assets).
• Establish data ownership, stewardship models, and domain accountability across AFM and Commercial.
• Drive data standardization, harmonization, and lifecycle management to enable consistent reporting and AI readiness.
• Lead enterprise-wide data governance frameworks, including policies, quality management, lineage, and metadata.
• Ensure compliance with regulatory, privacy, cybersecurity, and intellectual property standards.
• Define and track data quality KPIs and drive continuous improvement across business domains.
Data Platforms & Architecture
• Own the strategy and evolution of modern data platforms, including lakehouse architectures, real-time data pipelines, and semantic data layers.
• Ensure platforms are AI-ready, scalable, secure, and optimized for cost and performance.
• Partner with Enterprise Architecture and Cybersecurity to enforce standards, data models, and integration patterns.
• Enable seamless integration of ERP, CRM, supply chain, and engineering data into unified data products.
Analytics, AI & Advanced Capabilities
• Define and scale a portfolio of high-impact analytics and AI use cases, including:
o Commercial performance, pricing, and margin optimization
o Aftermarket demand forecasting and parts optimization
o Customer insights and segmentation
o Predictive maintenance and service optimization
o AI-enabled anomaly detection and operational intelligence
o Generative AI for commercial insights, automation, and decision support
• Lead the end-to-end AI lifecycle (ideation to production) with strong MLOps and governance practices.
• Establish a product-based data & analytics operating model, delivering reusable, scalable data products and AI capabilities.
AFM & Commercial Business Alignment
• Partner with AFM and Commercial leaders to translate business strategy into data, MDM, and AI solutions.
• Ensure master and transactional data enable core commercial processes including quoting, pricing, forecasting, and customer engagement.
• Drive use of data and AI to enhance revenue growth, profitability, and customer experience.
• Act as the primary data and AI leader for AFM and Commercial transformation initiatives.
Education and Qualifications
Required
• Bachelor's degree in Computer Science, Engineering, Data, or related field (Master's preferred).
• 12-15+ years of experience in enterprise data, MDM, analytics, and AI leadership roles.
• Proven track record leading enterprise-scale MDM and data transformation programs.
• Deep expertise in data governance, master data domains, and modern data architectures.
• Experience delivering AI/analytics solutions with measurable commercial impact.
• Strong leadership experience managing global, cross-functional teams and transformation programs.
Preferred
• Hands-on experience with MDM tools/platforms, data quality frameworks, and metadata management.
• Familiarity with AI/ML, MLOps, and GenAI applications in commercial or industrial settings.
• Experience in manufacturing, aftermarket, or asset-intensive industries.
• Exposure to OT/IT convergence and engineering data ecosystems.
• Strong executive presence with ability to influence at C-suite level.
Measures of Success
• Business value delivered through data, MDM, and AI initiatives (revenue, margin, cost, productivity)
• Enterprise data quality, consistency, and governance maturity
• Adoption and impact of analytics and AI in AFM and Commercial operations
• Speed and scalability of data product and AI delivery
• Effectiveness of MDM in enabling enterprise-wide insights and processes
Join our team of 28,000 problem solvers who are fostering a culture of innovation by leveraging the diverse perspectives of our global team. We believe in facing challenges head-on by finding opportunity and uncovering possibility, where roadblocks and barriers become targets instead of obstacles. We are One Dana with limitless opportunity.
Our Values
  • Value Others
  • Inspire Innovation
  • Grow Responsibly
  • Win Together

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