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Mlops Data Engineer Jobs in Michigan (NOW HIRING)

Data Engineer

Dearborn, MI · On-site

$105K - $127K/yr

D. or foreign equivalent degree in Computer Science, Software Engineering, Information System, Data Engineering, or a related field. * 2 years of experience with ML Model Development and/or MLOps.

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 ...

AI Data Engineer

Detroit, MI

$113K - $136K/yr

... MLOps: Experience with CI/CD, Docker, and ML lifecycle management tools like MLflow is highly ... We are seeking an experienced and highly skilled AI Data Engineer to join our team. The successful ...

ICT Data Engineer

Auburn Hills, MI

$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 ...

Build end-to-end AI workflows encompassing data ingestion, feature engineering, model training ... Establish and maintain MLOps practices, including automated training, deployment, monitoring ...

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 ...

... for MLOps, model governance, and lifecycle management. You will ensure AI solutions are not ... Feature & Data Strategy: * Guide design of features and data structures required for high ...

$95K - $130K/yr

You will help translate data science prototypes into secure, scalable, and production-ready ... influencing architecture, MLOps practices, and technical standards. This is an individual ...

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable, production-ready AI systems that generate measurable business value. The ideal candidate will have ...

As a Machine Learning Engineer, you will work within a collaborative technical team to build ... MLOps practices for versioning, orchestration, monitoring, and CI/CD. - Troubleshoot data, model ...

AI/ML and Data Engineer

Southfield, MI

$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 ...

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Showing results 1-20

Mlops Data Engineer information

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 Michigan? For Mlops Data Engineer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Mlops Data Engineer jobs in Michigan look for? The top searched job categories for Mlops Data Engineer jobs in Michigan are:
What cities in Michigan are hiring for Mlops Data Engineer jobs? Cities in Michigan with the most Mlops Data Engineer job openings:
Infographic showing various Mlops Data Engineer job openings in Michigan as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Data Engineer

$105K - $127K/yr

Full-time

Medical, Dental, Vision, Life, PTO

Posted 8 days ago


Job description

At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have an exciting opportunity for you to join our expanding area of Prognostics. 

Are you enthusiastic to mine raw data and realize its hidden value by building amazing, connected data solutions that benefit our customers? Would you love to accelerate our efforts in implementing advanced physics and ML Models in production?

The Data Engineer role resides within the Ford's Electric Vehicle organization. In this role, you will work on building scalable and robust data pipelines to process large volumes of connected vehicle data to support the Ford vehicle prognostic initiatives.

You will have...

  • Master's degree or foreign equivalent degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related field, and 4 years of experience OR equivalent combination of education and experience (6+ years with Bachelor's Degree). 
  • 4 years of professional experience in:
    • Data engineering, data product development and software product launches
    • At least three of the following languages: Java, Python, Spark, Scala, SQL
  • 3 years of cloud data/software engineering experience building scalable, reliable, and cost-effective production batch and streaming data pipelines using:
    • Data warehouses like Amazon Redshift, Microsoft Azure Synapse Analytics, Google BigQuery.
    • Workflow orchestration tools like Airflow.
    • Relational Database Management System like MySQL, PostgreSQL, and SQL Server.
    • Real-Time data streaming platform like Apache Kafka, GCP Pub/Sub
    • Microservices architecture to deliver large-scale real-time data processing application.
    • REST APIs for compute, storage, operations, and security.
    • DevOps tools such as Tekton, GitHub Actions, Git, GitHub, Terraform, Docker.
    • Project management tools like Atlassian JIRA.

Even better if you have...

  • Ph.D. or foreign equivalent degree in Computer Science, Software Engineering, Information System, Data Engineering, or a related field.
  • 2 years of experience with ML Model Development and/or MLOps.
  • Committed code to improve open-source data/software engineering projects
  • Experience architecting cloud infrastructure and handling application migrations/upgrades.
  • GCP Professional Certifications.
  • Demonstrated passion to mine raw data and realize its hidden value.
  • Passion to experiment/implement state of the art data engineering methods/techniques.
  • Experience working in an implementation team from concept to operations, providing deep technical subject matter expertise for successful deployment.
  • Experience implementing methods for automation of all parts of the pipeline to minimize labor in development and production.
  • Analytics skills to profile data, troubleshoot data pipeline/product issues.
  • Ability to simplify, clearly communicate complex data/software ideas/problems and work with cross-functional teams and all levels of management independently.
    You may not check every box, or your experience may look a little different from what we have outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!


You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!

As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder...or all of the above? No matter what you choose, we offer a work life that works for you, including:
Immediate medical, dental, vision and prescription drug coverage
Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
Vehicle discount program for employees and family members and management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
A generous schedule of paid holidays, including the week between Christmas and New Year's Day
Paid time off and the option to purchase additional vacation time.


For more information on salary and benefits, click here: https://fordcareers.co/GSRSP2 

This position is a range of salary grades 6-8 .

Visa sponsorship is not available for this position.

Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.

This position is hybrid. Candidates who are in commuting distance to a Ford hub location will be required to be onsite four or more days per week. 

#LI-Hybrid

#LI-CS2

What you will do...

  • Develop exceptional analytical data products using both streaming and batch ingestion patterns on Google Cloud Platform with solid data warehouse principles.
  • Build data pipelines to monitoring quality of data and performance of analytical models.
  • Maintain the infrastructure of the data platform using terraform and continuously develop, evaluate, and deliver code using CI/CD.
  • Collaborate with data analytics stakeholders to streamline the data acquisition, processing, and presentation process.
  • Implement an enterprise data governance model and actively promote the concept of data - protection, sharing, reuse, quality, and standards.
  • Enhance and maintain the DevOps capabilities of the data platform.
  • Continuously optimize and enhance existing data solutions (pipelines, products, infrastructure) for best performance, high security, low vulnerability, low costs, and high reliability.
  • Work in an agile product team to deliver code frequently using Test Driven Development (TDD), continuous integration and continuous deployment (CI/CD).
  • Promptly address code quality issues using SonarQube, Checkmarx, Fossa, and Cycode throughout the development lifecycle.
  • Perform any necessary data mapping, data lineage activities and document information flows.
  • Monitor the production pipelines and provide production support by addressing production issues as per SLAs.
  • Provide analysis of connected vehicle data to support new product developments and production vehicle improvements.
  • Provide visibility to data quality/vehicle/feature issues and work with the business owners to fix the issues.
  • Demonstrate technical knowledge and communication skills with the ability to advocate for well-designed solutions.
  • Continuously enhance your domain knowledge of connected vehicle data, connected services and algorithms/models developed by data scientists within Ford.
  • Stay current on the latest data engineering practices and contribute to the technical direction of the company while keeping a customer-centric approach.

Ford logo

About Ford

Sourced by ZipRecruiter

At Ford Motor Company, we believe freedom of movement drives human progress. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career and help us define tomorrow's transportation.

Industry

Civil engineering construction

Company size

51 - 200 Employees

Headquarters location

Doral, FL, US

Year founded

1982