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Mlops Jobs in Bothell, WA (NOW HIRING)

Microsoft

Software Engineer

Redmond, WA · On-site

$111K - $183K/yr

Data & MLOps Foundations * Build and maintain data pipelines, ETL workflows, and training datasets for AI model development. * Support deployment of models using MLOps practices (CI/CD, basic model ...

Alaska Airlines

Manager, AI

Seatac, WA · On-site

$149K - $224K/yr

Guide the adoption of MLOps practices for both traditional and GenAI model deployment, versioning, monitoring, and retraining. * Stay current with advancements in LLMs (Large Language Models ...

Alaska Airlines

Manager, AI

Seatac, WA · On-site

$149K - $224K/yr

Guide the adoption of MLOps practices for both traditional and GenAI model deployment, versioning, monitoring, and retraining. * Stay current with advancements in LLMs (Large Language Models ...

Apex Systems

Sr AI Analyst

Seattle, WA · On-site

$100K - $132K/yr

Support engineering and MLOps teams to transition prototypes into production. * Produce user-facing documentation, training, and adoption materials. * Report outcomes and ROI to stakeholders and ...

Alaska Airlines

Manager, AI

Seattle, WA · On-site

$149K - $224K/yr

Guide the adoption of MLOps practices for both traditional and GenAI model deployment, versioning, monitoring, and retraining. * Stay current with advancements in LLMs (Large Language Models ...

Saransh Inc

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117K - $162K/yr

Cloud & MLOps * Deploy and manage AI workloads on Azure or AWS, including serverless inference endpoints, container registries, and GPU/compute resources. * Build and maintain CI/CD pipelines for AI ...

Staffingine LLC

Data Engineer

Redmond, WA · On-site

$128K - $154K/yr

Exposure to DataOps or MLOps practices is a plus. * Azure Data Engineer or related Microsoft certification preferred.

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Mlops information

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 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 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.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are popular job titles related to Mlops jobs in Bothell, WA?

For Mlops jobs in Bothell, WA, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in Bothell, WA look for?

The top searched job categories for Mlops jobs in Bothell, WA are:

What cities near Bothell, WA are hiring for Mlops jobs?

Cities near Bothell, WA with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Bothell, WA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 84% In-person, and 16% Remote job distribution.

Software Engineer

Microsoft

Redmond, WA • On-site

$111K - $183K/yr

Full-time

Re-posted 11 days ago

Microsoft rating

8.5

Company rating: 8.5 out of 10

Based on 132 frontline employees who took The Breakroom Quiz

79th of 245 rated software companies

Job description

Overview
Microsoft Security (MSEC) is seeking an AI Engineer II to help design and build AI-native systems that enable customers to securely adopt AI at enterprise scale.
This role sits at the intersection of AI engineering and real-world application, where you will contribute to building intelligent systems that transform signals across identity, devices, data, applications, and infrastructure into actionable insights and automation.
You will work as part of a collaborative engineering team to develop, deploy, and improve AI systems-from model development and data pipelines to production deployment and monitoring-while growing your expertise in modern AI technologies and enterprise systems.
Responsibilities
Responsibilities
AI Systems & Model Development
  • Contribute to building AI/ML models, including LLM-based and RAG systems, to solve customer and business problems.

  • Implement components of AI-driven systems and workflows, translating requirements into working solutions.

  • Assist in transforming multi-source data into contextual intelligence and automation pipelines.

Data & MLOps Foundations
  • Build and maintain data pipelines, ETL workflows, and training datasets for AI model development.

  • Support deployment of models using MLOps practices (CI/CD, basic model versioning, containerized environments).

  • Monitor and troubleshoot AI systems for performance, reliability, and data quality issues.

AI Readiness & Customer Solutions
  • Contribute to building AI-powered solutions and tools that help customers adopt AI capabilities securely and effectively.

  • Support implementation of AI readiness metrics, telemetry, and reporting under guidance from senior engineers.

  • Assist in integrating AI capabilities into services, APIs, and applications.

Builder Mindset & Growth
  • Demonstrate a builder mindset with a bias for action, rapidly prototyping features and iterating based on feedback.

  • Learn to operate in ambiguous problem spaces, breaking down tasks into incremental deliverables.

  • Continuously improve through experimentation, debugging, and telemetry-driven insights.

Collaboration & Execution
  • Collaborate with engineering, data science, and product teams to deliver AI-driven features and solutions.

  • Participate in design discussions, code reviews, and team execution processes.

  • Contribute to integration efforts across systems, services, and APIs.

Engineering Excellence
  • Write clean, maintainable, and well-tested code for AI systems and data pipelines.

  • Document system design, model behavior, and implementation details for team knowledge sharing and maintainability.

  • Follow best practices for security, compliance, and responsible AI development.

Qualifications
Required Qualifications:
  • Bachelor's Degree in Computer Science, or related technical discipline with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Preferred Qualifications:
  • Experience building or contributing to AI/ML models or data-driven systems.

  • Programming experience in Python or similar languages for AI development.

  • Understanding of machine learning fundamentals and data processing workflows.

  • Experience with LLMs, RAG pipelines, or vector databases (coursework or project experience acceptable).

  • Exposure to MLOps practices (CI/CD, model deployment, monitoring).

  • Familiarity with cloud platforms (Azure preferred) for AI/ML workloads.

  • Strong problem-solving skills and ability to learn quickly in fast-evolving AI domains.

Software Engineering IC2 - The typical base pay range for this role across the U.S. is USD $85,400 - $168,100 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $111,100 - $183,700 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

What Microsoft employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Microsoft

Sourced by ZipRecruiter

Our infrastructure is comprised of a large global portfolio of more than 100 datacenters and 1 million servers. Our foundation is built upon and managed by a team of subject matter experts working to support services for more than 1 billion customers and 20 million businesses in over 90 countries worldwide. With environmental sustainability and optimization at the forefront of our datacenter design and operations, we continue to grow and evolve as we meet the ever-changing business demands that hold Microsoft as a world-class cloud provider.

Industry

Computer and computer peripheral equipment and software wholesalers

Company size

10,000+ Employees

Headquarters location

Redmond, WA, US

Year founded

1975

Social media

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