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Mlops Jobs in Rochester, MI (NOW HIRING)

ALTEN Technology USA

Practice Manager - AI & Data

Troy, MI · On-site

$160K - $190K/yr

MLOps / LLMOps tools (MLflow, Kubeflow, containerization, orchestration) * Understanding of data governance, security, and AI regulations Skills & Competency Management * Create, maintain, and manage ...

Stellantis

Data Scientist

Auburn Hills, MI · On-site

Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines * Strong grasp of machine learning algorithms like: * Regression (linear, logistic) * Causal ...

Stellantis

Machine Learning Engineer

Auburn Hills, MI

$108K - $130K/yr

Exposure to MLOps or model productionization Our Benefits -- Designed with You in Mind Comprehensive Health & Well-being Coverage From your very first day, you'll have access to medical, dental ...

Maddisoft

AI Specialist

Rochester, MI · On-site

Experience with MLOps, CI/CD pipelines, and AI model lifecycle management. Familiarity with change management methodologies and organizational transformation initiatives. AI, Cloud, or Data Science ...

Stellantis

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 ...

Maddisoft

AI Specialist

Rochester, MI · On-site

Experience with MLOps, CI/CD pipelines, and AI model lifecycle management. Familiarity with change management methodologies and organizational transformation initiatives. AI, Cloud, or Data Science ...

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Showing results 21-40

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Mlops information

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

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

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

What are popular job titles related to Mlops jobs in Rochester, MI?

For Mlops jobs in Rochester, MI, the most frequently searched job titles are:

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

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

Mlops jobs near you
Infographic showing various Mlops job openings in Rochester, MI as of August 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Contract. Highlights an 71% Physical, 10% Hybrid, and 19% Remote job distribution.

Engineering Assistant- AI Based Solutions

FEV North America, Inc

Auburn Hills, MI • On-site

Full-time

Posted 29 days ago

Job description

  • Develop and deploy AI-based solutions to automate engineering and business workflows, improving efficiency, decision-making, and productivity
  • Identify opportunities to replace manual or rule-based processes with intelligent AI-driven workflows and agents
  • Design, develop, and integrate machine learning (ML), reinforcement learning (RL), and generative AI models for engineering applications
  • Replace conventional rule-based control algorithms and physics-based functions with data-driven ML/RL models where appropriate
  • Develop data pipelines for collection, cleaning, feature engineering, training, validation, and deployment of AI models
  • Train, optimize, and validate ML/RL models using simulation, test, and field data
  • Collaborate with controls, software, systems, and domain experts to integrate AI models into production systems
  • Support development of digital twins, predictive analytics, anomaly detection, optimization, and intelligent decision-making systems
  • Monitor model performance, perform retraining activities, and ensure robustness and scalability of deployed solutions
  • Prepare technical reports, documentation, presentations, and demonstrations for internal and customer stakeholders
  • Stay current with emerging AI technologies, frameworks, and best practices and evaluate their applicability to engineering challenges

Requirements
  • Working towards Bachelor's or master's degree in computer science, Electrical Engineering, Mechanical Engineering, Robotics, Data Science, Artificial Intelligence, or a related field
  • Understanding of Machine Learning, Deep Learning, Reinforcement Learning, and Generative AI concepts
  • Proficiency in Python and common AI/ML frameworks such as TensorFlow, PyTorch, and RL libraries
  • Experience with software development tools, version control systems, and CI/CD processes
  • Experience with data processing, feature extraction, model training, validation, and deployment workflows

Preferred Qualifications:
  • Knowledge of optimization techniques, control systems, and system modeling concepts
  • Familiarity with cloud-based AI platforms and MLOps practices is preferred
  • Strong analytical and problem-solving skills with the ability to work on complex engineering challenges
  • Professional communication skills (oral and written) and ability to present technical concepts to diverse audiences

Equal opportunity employer as to all protected groups, including protected veterans and individuals with disabilities

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