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Senior Machine Learning Ops Engineer Jobs in Minnesota

Onsights

Senior Machine Learning Engineer

Minneapolis, MN · On-site

$109K - $149K/yr

As a Senior Machine Learning Engineer, you will design, develop, test, document, deploy, and maintain production machine learning and statistical modeled software to automate processes and streamline ...

Dahl Consulting

Machine Learning Engineer

Brooklyn Park, MN · On-site

$60.15 - $75.18/hr

Machine Learning Engineer Location : Brooklyn Park, MN Job Type : Contract (4 Months) Compensation : $60.15 - $75.18/hr Industry: Retail --- About the Role We are partnering with a leading national ...

Jobtailor

Machine Learning Engineer

Virginia, MN · On-site

$150 - $190/hr

Guide clients in navigating ML algorithms, tools, and frameworks Requirements * 2+ years of experience with artificial intelligence, data science, or machine learning engineering * Experience with ...

Tennant Company

Machine Learning Engineer

Golden Valley, MN · On-site

$119K - $122K/yr

Tennant Company seeks a full-time Machine Learning Engineer based in Golden Valley, MN. Responsible for utilizing experience in emerging technologies such as Cloud computing, Big Data, Deep Machine ...

Kitware

Machine Learning Engineer

Minneapolis, MN · On-site

$85K - $125K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Tennant Company

Machine Learning Engineer

Golden Valley, MN · Hybrid

$119K - $122K/yr

Tennant Company seeks a full-time Machine Learning Engineer based in Golden Valley, MN. Responsible for utilizing experience in emerging technologies such as Cloud computing, Big Data, Deep Machine ...

Kitware

Machine Learning Engineer

Minneapolis, MN · On-site

$85K - $125K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Kitware

Machine Learning Engineer

Minneapolis, MN

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

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Senior Machine Learning Ops Engineer information

What is a senior machine learning ops engineer?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by senior machine learning ops engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What are the most commonly searched types of Machine Learning Ops Engineer jobs in Minnesota?

The most popular types of Machine Learning Ops Engineer jobs in Minnesota are:

What are popular job titles related to Senior Machine Learning Ops Engineer jobs in Minnesota?

For Senior Machine Learning Ops Engineer jobs in Minnesota, the most frequently searched job titles are:

What job categories do people searching Senior Machine Learning Ops Engineer jobs in Minnesota look for?

The top searched job categories for Senior Machine Learning Ops Engineer jobs in Minnesota are:

What cities in Minnesota are hiring for Senior Machine Learning Ops Engineer jobs?

Cities in Minnesota with the most Senior Machine Learning Ops Engineer job openings:

Senior Machine Learning Ops Engineer jobs near you
Infographic showing various Senior Machine Learning Ops Engineer job openings in Minnesota as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 18% Part Time, and 5% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.

Senior Machine Learning Engineer

Onsights

Minneapolis, MN • On-site

$109K - $149K/yr

Full-time

Re-posted 11 days ago

Job description

Job Summary:
Anno.ai is a mission-focused defense technology startup dedicated to accelerating the safe and effective development of next-generation autonomous systems. As a Senior Machine Learning Engineer, you will design, develop, test, document, deploy, and maintain production machine learning and statistical modeled software to automate processes and streamline customer mission operations.
Responsibilities:
• Operationalize machine learning models by building and maintaining robust, scalable pipelines for training, evaluation, deployment, and lifecycle management across cloud, on-prem, and edge compute environments
• Work closely with autonomy researchers, software engineers, systems teams, and field operators to translate mission requirements into deployable ML capabilities
• Implement automated CI/CD workflows tailored to ML systems, ensuring repeatable experiments, reliable packaging, and continuous delivery of both up to date models and associated data pipelines
• Manage ML runtime infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes) and incorporating model serving platforms (e.g., Seldon, KServe, BentoML)
• Develop monitoring systems to track model health, performance, data drift, system utilization, and mission relevance using tools such as Prometheus, Grafana, and ELK/EFK stacks
• Ensure ML deployments meet defense, customer, and platform security requirements, with emphasis on data integrity, traceability, and operational reliability
• Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to enhance reproducibility, extensibility, scalability, and deployment speed of ML systems
Qualifications:
Required:
• Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a related technical field (Master's preferred)
• 5+ years of professional experience in software engineering, machine learning engineering, MLOps, or related roles
• Experience operationalizing ML systems at production scale, including model training, versioning, packaging, deployment, and monitoring
• Strong proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow)
• Hands-on experience with MLOps frameworks and workflow tooling (e.g., MLflow, Kubeflow, Airflow, DVC, BentoML)
• Experience deploying containerized ML services using Docker and orchestrating workloads using Kubernetes (including air-gapped or constrained deployments)
• Understanding of CI/CD workflows and DevOps practices applied to ML systems (e.g., Git, Code Review, Metrics Evaluation)
• Familiarity with monitoring, observability, and logging platforms (e.g., Prometheus, Grafana, ELK/EFK)
• Ability to obtain and maintain U.S. Government security clearance (U.S. Citizenship required)
• Ability to travel up to 20%
Preferred:
• Experience with deploying models and associated runtimes to Edged Devices
• Experience optimizing models for memory and CPU constrained systems (e.g., embedded systems, microcontrollers)
• Prior experience supporting U.S. Department of War programs, cUAS systems, or mission-critical autonomous platforms
• Experience working with diverse or atypical data sources (e.g., Audio/Acoustics, RF signals, EO/IR imagery)
• Experience deploying and optimizing ML inference on edge or resource-limited compute systems
• Experience with Explainable/Auditable AI/ML tools and interpretable model design
• Experience with AI Software Development Tools (e.g., GitHub CoPilot, Claude)
Company:
Online analytics for the physical world. Built on the tech you already own. Stop buying tools and start driving measurable progress. Founded in 2019, the company is headquartered in Minnetonka, USA, with a team of 11-50 employees. The company is currently Early Stage.

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