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Senior Mlops Engineer Jobs in Redmond, WA (NOW HIRING)

Empower Professionals

Sr AI Engineer

Bellevue, WA · On-site

$138.30K - $182.30K/yr

Role : Senior AI Engineer - Privacy Duration: 12+ Month Location : Bellevue, WA AI Agent & LLM ... Cloud & MLOps * MsoNormal">Deploy and manage AI workloads on Azure or AWS, including serverless ...

Merican

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117.90K - $162K/yr

Senior AI Engineer - Privacy The Senior AI Engineer - Privacy will design, build, and ... Cloud & MLOps * Deploy and manage AI workloads on Azure or AWS, including serverless inference ...

Tekwissen

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117.90K - $162K/yr

Senior AI Engineer - Privacy Location: Bellevue, WA Duration: 6 Months Job Type: Temporary ... Cloud & MLOps * Deploy and manage AI workloads on Azure or AWS, including serverless inference ...

Merican

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117.90K - $162K/yr

Senior AI Engineer - Privacy Location: Bellevue, WA Duration: 6 Months Job Type: Temporary ... Cloud & MLOps * Deploy and manage AI workloads on Azure or AWS, including serverless inference ...

Deloitte

AI Engineer Senior Consultant

Seattle, WA · Hybrid

$118.90K - $163.30K/yr

Contribute to MLOps/LLMOps and production operations (versioning, reproducibility, CI/CD, automated ... AI Engineer Senior Consultant Our Deloitte Human Capital team transforms technology platforms ...

Deloitte

AI Data Engineer - Senior Consultant

Seattle, WA · Hybrid

$118.90K - $163.30K/yr

Contribute to MLOps/LLMOps and production operations (versioning, reproducibility, CI/CD, automated ... AI Engineer Senior Consultant Our Deloitte Human Capital team transforms technology platforms ...

Lululemon athletica

Senior AI/ML Engineer

Seattle, WA · On-site

$118.90K - $163.30K/yr

Core responsibilities As a Senior AI/ML Engineer, you will lead the delivery of scalable AI/ML ... Developing reusable MLOps components to support experimentation, deployment, monitoring, and ...

lululemon

Senior AI/ML Engineer

Seattle, WA · On-site

$119K - $163.40K/yr

Core responsibilities As a Senior AI/ML Engineer, you will lead the delivery of scalable AI/ML ... Developing reusable MLOps components to support experimentation, deployment, monitoring, and ...

Lululemon

Senior AI/ML Engineer

Seattle, WA · On-site

$118.90K - $163.30K/yr

Core responsibilities As a Senior AI/ML Engineer, you will lead the delivery of scalable AI/ML ... Developing reusable MLOps components to support experimentation, deployment, monitoring, and ...

lululemon

Senior AI/ML Engineer

Seattle, WA

$118.90K - $163.30K/yr

Core responsibilities As a Senior AI/ML Engineer, you will lead the delivery of scalable AI/ML ... MLOps and observability standards. You will be the technical authority for ML engineering ...

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Senior Mlops Engineer information

See Redmond, WA salary details

$66.6K

$141.7K

$205.5K

How much do senior mlops engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for senior mlops engineer in Redmond, WA is $141,737.00, according to ZipRecruiter salary data. Most workers in this role earn between $117,000.00 and $160,700.00 per year, depending on experience, location, and employer.

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

To thrive as a Senior MLOps Engineer, you need deep expertise in machine learning workflows, software engineering, and cloud infrastructure, typically supported by a degree in computer science or related fields. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, and platforms such as AWS, GCP, or Azure, as well as certifications in cloud or DevOps, are highly valuable. Strong problem-solving, collaboration, and communication skills set standout professionals apart in this role. These skills and qualities are crucial to ensuring robust, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges Senior MLOps Engineers face when deploying machine learning models to production environments?

Senior MLOps Engineers often encounter challenges such as managing model versioning, ensuring reproducibility, and scaling deployments across diverse infrastructure. Balancing the needs of data scientists for experimentation with the stability and reliability requirements of production systems can be complex. Additionally, integrating continuous integration and continuous deployment (CI/CD) pipelines for ML workflows and monitoring model performance post-deployment are ongoing responsibilities. Collaboration with data scientists, software engineers, and IT operations is crucial to address these challenges and maintain robust, efficient ML systems.

What is a Senior MLOps Engineer?

A Senior MLOps Engineer is an experienced professional who bridges the gap between data science, machine learning, and software engineering. They are responsible for designing, deploying, and maintaining scalable machine learning systems in production environments. Their role involves automating workflows, monitoring model performance, ensuring reproducibility, and managing the infrastructure needed to support machine learning operations. Senior MLOps Engineers also collaborate with data scientists, software developers, and IT teams to ensure smooth integration and continuous delivery of ML models. They play a crucial role in making machine learning solutions reliable, efficient, and scalable for business applications.

What is the difference between Senior Mlops Engineer vs Data Scientist?

AspectSenior Mlops EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, Engineering, or related; experience with ML deployment toolsBachelor's/Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in productionFocus on data analysis, model development, and insights generation
Industry UsageUsed in tech, finance, healthcare for ML deploymentUsed across industries for data analysis and modeling

The main difference is that Senior Mlops Engineers specialize in deploying and maintaining machine learning models in production environments, while Data Scientists focus on developing models and analyzing data. Both roles require strong technical skills, but their day-to-day tasks and focus areas differ significantly.

What are the most commonly searched types of Mlops Engineer jobs in Redmond, WA? The most popular types of Mlops Engineer jobs in Redmond, WA are:
What are popular job titles related to Senior Mlops Engineer jobs in Redmond, WA? For Senior Mlops Engineer jobs in Redmond, WA, the most frequently searched job titles are:
What job categories do people searching Senior Mlops Engineer jobs in Redmond, WA look for? The top searched job categories for Senior Mlops Engineer jobs in Redmond, WA are:
What cities near Redmond, WA are hiring for Senior Mlops Engineer jobs? Cities near Redmond, WA with the most Senior Mlops Engineer job openings:
Senior Mlops Engineer jobs near you

MLOps Engineer - Hourly and Full time (106-05SENG-02)

OPS Brasil

Seattle, WA • On-site, Remote

Contractor

Posted 3 days ago

Job description

Cloudary is hiring a Senior ML Engineer with strong data engineering foundations and hands-on MLOps experience for a contract engagement with an international client. You'll own ML pipelines and model serving infrastructure - bridging data and production ML at scale.
  • 5+ years in data engineering (pipelines, warehouses, orchestration)
  • 2+ years of hands-on ML engineering / MLOps in production environments
  • Strong Python skills and experience with Airflow, Spark, or similar orchestration tools
  • Solid knowledge of Kubernetes, Docker, and at least one major cloud (AWS, GCP or Azure)
  • Familiarity with ML tooling: MLflow, W&B, DVC, or equivalent
  • Available full-time (80h/week), EST-aligned, eligible to contract in Canada (CAD)
Responsabilities
  • Design and maintain end-to-end ML pipelines from data ingestion to model deployment
  • Operate model registries, feature stores, and experiment tracking (MLflow, W&B)
  • Build scalable model serving infrastructure on Kubernetes and cloud platforms
  • Implement CI/CD workflows for ML models, including testing and rollback strategies
  • Monitor production models - drift detection, alerting, and retraining pipelines
  • Collaborate with data scientists and platform engineers to ship ML solutions faster

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