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

Stellantis

ICT Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

Exposure to MLOps concepts, model deployment, or monitoring * Hands-on experience with Palantir Foundry, Snowflake Intelligence * Master's degree in Data Science, Statistics, Engineering, Computer ...

Stellantis

ICT Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

... MLOps concepts, model deployment, or monitoring • Hands-on experience with Palantir Foundry ... Data Science, Statistics, Engineering, Computer Science, or related field. Company : Our storied ...

company826

AI/ML and Data Engineer

Southfield, MI · On-site +1

$104K - $125K/yr

Establish and mature MLOps/LLMOps practices, including CI/CD, model and prompt versioning ... Extensive data engineering experience, including pipeline development, database design, and ...

SME

AI/ML and Data Engineer

Southfield, MI · On-site

$104K - $125K/yr

Establish and mature MLOps/LLMOps practices, including CI/CD, model and prompt versioning ... Provide executive-level advisory services on AI adoption and data modernization, tailoring ...

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

FTE Factory Advisors

AI Systems Engineer

Detroit, MI · On-site

... systems, data sources, and vendor-managed platforms * Practical experience with MLOps, system ... engineering in production environments * Experience with Cloud, NoSQL Databases, and Microsoft ...

FTE Factory Advisors

AI Systems Engineer

Detroit, MI · On-site

... systems, data sources, and vendor-managed platforms * Practical experience with MLOps, system ... engineering in production environments * Experience with Cloud, NoSQL Databases, and Microsoft ...

FTE Factory Advisors

AI Systems Engineer

Detroit, MI

... systems, data sources, and vendor-managed platforms * Practical experience with MLOps, system ... engineering in production environments * Experience with Cloud, NoSQL Databases, and Microsoft ...

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Showing results 1-20

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 Jul 9, 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 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 are popular job titles related to Mlops Data Engineer jobs in Rochester, MI? For Mlops Data Engineer jobs in Rochester, MI, the most frequently searched job titles are:
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
ICT Data Engineer

ICT Data Engineer

Stellantis

Auburn Hills, MI • On-site

$108K - $130K/yr

Full-time

Posted 7 days ago

Stellantis rating

7.4

Company rating: 7.4 out of 10

Based on 126 frontline employees who took The Breakroom Quiz

17th of 44 rated automakers

Job description

We are seeking a strategic and hands-on Data Engineer to support Purchasing and Finance Analytics and Programs within our North America Data & AI team. Data engineering is the practice of making the appropriate data available to various data consumers (including data scientists, data and business analysts, citizen integrators, and line-of-business users). It is a discipline that involves collaboration across business and IT units.
In addition to creating and maintaining an optimal pipeline architecture, typical duties and responsibilities for a Data Engineer position may include:
The ideal candidate combines strong analytical skills with practical experience building scalable analytics, models, and data products in enterprise environments. You will be part of a talented team of data scientists, engineers, driving predictive analytics and early detection of emerging warranty trends using vast datasets across the enterprise.
Key Responsibilities:
  • Assembling large, complex sets of data that meet non-functional and functional business requirements
  • Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
  • Develop robust ETL (Extract, Transform, Load) process to integrate data from various sources.
  • Identifying, designing and implementing internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
  • Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS, Azure, DB2 and SQL technologies
  • Building scalable tables to provide actionable insight into key business performance metrics including operational efficiency and customer acquisition
  • Working with stakeholders including the Data Product teams to support their data infrastructure needs while assisting with data-related technical issues
  • Design and maintain data models, schemas, and database structures to support analytical and operational use cases.
  • Optimize data storage and retrieval mechanisms for performance and scalability.
  • Lead and coordinate cross-functional AI programs from concept to deployment, ensuring alignment with business goals and timelines.
  • Collaborate with other data scientists, engineers, and business stakeholders to define and prioritize program objectives.
  • Apply statistical analysis and machine learning techniques to solve business and operational problems.
  • Partner with business stakeholders to understand requirements and translate them into analytical solutions.
  • Translate business needs into actionable AI use cases and technical requirements
  • Build and deploy predictive models to forecast warranty claims, failure rates, and cost trends.
  • Ensure data quality, lineage, documentation, and compliance with governance requirements
  • Create dashboards and analytical outputs that drive insight adoption and operational impact
  • Collaborate with business data engineers, and platform teams on scalability, performance, and best practices

Basic Qualifications
  • Bachelor's or in Data Science, Statistics, Engineering, Computer Science, or related field.
  • Minimum 3 years' experience as Data Scientist, Advanced Analyst, or similar role
  • Strong proficiency in Python, SQL, PySpark and visualization tools (e.g., Power BI, Foundry Workshop).
  • Solid understanding of statistics, exploratory data analysis, and applied machine learning.
  • Experience working with large, complex datasets in enterprise environments
  • Ability to communicate analytical findings clearly to technical and non-technical audiences.
  • Proven experience delivering end-to-end analytics or data science solutions into production.
  • Experience with one or two data and cloud platforms (e.g., Palantir Foundry. Snowflake, Databricks AWS, Azure, GCP).
  • Strong communication and stakeholder engagement skills.

Preferred Qualifications
  • Familiarity with data modeling, semantic layers, and enterprise data platforms.
  • Industry experience in automotive and manufacturing
  • Exposure to MLOps concepts, model deployment, or monitoring
  • Hands-on experience with Palantir Foundry, Snowflake Intelligence
  • Master's degree in Data Science, Statistics, Engineering, Computer Science, or related field.
  • This is a fast-paced environment providing rapid delivery for our business partners. You will be working in a highly collaborative environment that values speed and quality, with a strong desire to drive change and value.

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