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Mlops Jobs in Rochester, MI (NOW HIRING)

Stellantis

Solution Architect

Auburn Hills, MI · On-site

$59.50 - $78.50/hr

Hands-on MLOps/platform experience (CI/CD for ML, model registry, feature store, monitoring & drift detection). * Experience in enterprise-scale, regulated environments and/or implementing ...

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, monitoring/observability, rollback procedures, and cost/performance optimization for production environments.

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, monitoring/observability, rollback procedures, and cost/performance optimization for production environments.

Deloitte

Google AI Lead Architect

Detroit, MI

$54.75 - $75/hr

Define end-to-end architectures across data pipelines, feature engineering, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build. * Lead cloud-native ...

Canopy

Senior Data Analyst

Detroit, MI · Remote

$96K - $132K/yr

Experience with MLOps practices, including model deployment, monitoring, and lifecycle management in production environments. * Reside within the Detroit area or nearby, with the ability to work in a ...

Ford Motor Company

Senior Data Engineer

Redford, MI · On-site

$85K - $192K/yr

MLOps: Familiarity with CI/CD for machine learning, containerization (Docker/Kubernetes), and model monitoring tools. * Communication: Ability to explain complex mathematical concepts to non ...

JDA

Sr Gen AI Engineer

Southfield, MI · On-site

$87K - $140K/yr

Background in MLOps, model deployment, or model serving infrastructure * Expertise in optimizing high-performance, low-latency systems * Knowledge of prompt engineering and AI safety/alignment ...

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All JobsMlops JobsMlops Jobs in Rochester, MI

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.

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

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.

Is MLOps a good career path?

MLOps is a growing field that combines machine learning, software engineering, and operations to deploy and maintain AI models efficiently. It offers high demand for skills in cloud platforms, automation, and data management, making it a promising career choice for those interested in AI infrastructure. Professionals in MLOps often work with tools like Docker, Kubernetes, and CI/CD pipelines, and typically require a strong understanding of both machine learning and software development.

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 engineers make $500,000?

Senior machine learning operations (MLOps) engineers with extensive experience, specialized skills in cloud platforms, automation, and deployment often reach or exceed $500,000 annually in total compensation. High-level roles in tech companies or those with leadership responsibilities and advanced certifications tend to offer such salaries.

Which 3 jobs will survive AI?

For MLOps professionals, roles such as data scientists, machine learning engineers, and AI infrastructure engineers are expected to persist as AI adoption grows. These jobs require specialized skills in model development, deployment, and maintenance that complement automation. Continuous learning and expertise in tools like Kubernetes, cloud platforms, and version control are essential for long-term viability.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI director, often requiring advanced skills in data science, deep learning, and cloud platforms. These roles usually involve leadership, strategic planning, and extensive experience, and they may include bonuses or stock options that contribute to the total compensation. Such salaries are rare and generally found in large tech companies or specialized AI firms.

What are 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
Industrial IoT and Wireless Network Systems Researcher

Industrial IoT and Wireless Network Systems Researcher

Optimal Inc.

Warren, MI • On-site

$97K - $133K/yr

Full-time

Posted 4 days ago

Job description

Senior Researcher/ Engineer - 5G/6G Industrial IoT and Wireless Network Systems

Role Summary
We are seeking a Senior Researcher / Senior Engineer to lead the architecture and development of next-generation wireless data infrastructure for Future Factory applications. This role will focus on moving high-fidelity data from wireless sensors through 5G and 6G networks into industrial IoT and edge-AI environments with millisecond-level latency, high reliability, and secure scalable integration.
The successful candidate will build the connectivity and data-transfer backbone that links advanced wireless sensors to edge computing, IoT platforms, and AI models for real-time manufacturing intelligence.

What You'll Do
Design end-to-end architecture for transferring data from wireless industrial sensors through 5G/6G networks to edge compute and IoT platforms
Define communication strategies for low-latency, high-reliability, secure transport of precision manufacturing data
Develop and evaluate private 5G and future 6G network concepts for manufacturing use cases including inline monitoring, machine condition tracking, process control, and AI-based quality prediction
Select and implement protocols, gateways, edge services, and data pipelines for integrating wireless sensor streams into industrial IoT systems
Build solutions for time-sensitive acquisition, buffering, synchronization, prioritization, and quality-of-service management of sensor data
Partner with sensor, controls, manufacturing, cybersecurity, and AI teams to ensure end-to-end performance from measurement to inference
Evaluate network latency, packet loss, jitter, bandwidth, device density, handoff behavior, and scalability under realistic factory conditions
Develop architectures for edge AI deployment so that data can be processed and acted on within milliseconds when needed
Support integration with cloud and plant systems using modern telemetry and industrial communication frameworks
Lead pilot deployments, technical validation, and roadmap development for 5G/6G-enabled smart manufacturing systems

Required Qualifications


Master's degree or PhD in Electrical Engineering, Computer Engineering, Computer Science, Telecommunications, Networking, Robotics, or a related field
5+ years of industrial or applied research experience in wireless communications, industrial IoT, edge systems, or networked cyber-physical systems
Strong expertise in wireless communication systems, especially 5G and emerging next-generation mobile network technologies
Experience designing end-to-end data pipelines from device or sensor layer to edge or cloud applications
Strong understanding of latency, reliability, synchronization, bandwidth management, and network performance tradeoffs for real-time systems
Experience with IoT data protocols and messaging frameworks such as MQTT, OPC UA, DDS, AMQP, gRPC, or similar technologies
Experience with edge computing architectures and deployment of analytics or AI near the point of data generation
Strong software and system-integration skills in Python, C++, embedded Linux, or related environments
Ability to work across hardware, software, manufacturing, and analytics teams to build deployable industrial systems

Preferred Qualifications


PhD or advanced specialization in wireless networking, industrial communications, or edge intelligence
Experience with private 5G deployment, network slicing, URLLC concepts, time-sensitive networking, or deterministic industrial communications
Familiarity with 6G research trends relevant to sensing, localization, ultra-low-latency networking, or integrated communication and sensing
Experience integrating plant-floor systems with IoT platforms, edge gateways, and AI services
Knowledge of industrial cybersecurity, zero-trust architectures, device authentication, and secure data transport
Experience with digital twins, streaming analytics, event-driven architectures, or MLOps for real-time manufacturing systems
Experience in automotive manufacturing, industrial automation, robotics, or smart factory deployments

Key Skills and Competencies


5G/6G wireless systems
Industrial IoT architecture
Edge computing and real-time data pipelines
Low-latency network design
Protocol and gateway integration
Network performance validation
Cyber-physical systems engineering
Cross-functional systems leadership