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Mlops Engineer Jobs in Oregon (NOW HIRING)

Nike

Senior Principal Software Engineer

Beaverton, OR · On-site

$130K - $180K/yr

Experience with modern ML stacks (e.g., LLMs, PyTorch, TensorFlow, Spark, and cloud-native MLOps tools) * Strong track record with modern DevOps methodologies, automation, CI/CD pipelines, and ...

Versapay

Senior Data Engineer

OR · On-site +1

$105K - $143K/yr

As a Senior Data Engineer, you will be pivotal in optimizing and scaling our foundational Snowflake ... Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle ...

Anno.ai

Senior Machine Learning Engineer

OR · On-site +1

$104K - $143K/yr

Due to the sensitive nature of our engineering work, Anno.ai enforces strict digital footprint and ... Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to ...

OneStudyTeam

Senior Machine Learning Engineer

OR · On-site +1

$140K - $190K/yr

By joining our team as a Senior Machine Learning Engineer , you will play a pivotal role in ... Leverage modern cloud tools and MLOps best practices to build robust data pipelines and deploy ...

Redapt

Forward Deployed Engineer, Agentic AI

OR · On-site +1

Forward Deployed Engineer, Agentic AI About the Role Redapt is building dedicated capacity to ... Experience with MLOps/LLMOps practices, including model monitoring and prompt management. * Prior ...

Tebra

Data Engineer

OR · On-site +1

$114K - $137K/yr

Collaborate with ML Engineers and cross-functional partners to support MLOps best practices, including data versioning, lineage, and reproducibility. * Break down technical work into manageable tasks ...

Particle41

AI Engineer

OR · On-site +1

AI Engineer Role Overview: As an AI Engineer at Particle41 you will design, develop and deploy ... Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker). * Strong ...

Fisher Investments

AI Engineering Manager

Portland, OR · Hybrid

Establish engineering best practices for AI development, including MLOps, LLMOps, testing, observability, security, governance, and responsible AI * Evaluate latest AI technologies and frameworks ...

Fisher Investments

AI Engineering Manager

Gresham, OR · Hybrid

Establish engineering best practices for AI development, including MLOps, LLMOps, testing, observability, security, governance, and responsible AI * Evaluate latest AI technologies and frameworks ...

SentinelOne

Sr. Manager, AI Software Engineering

OR · On-site +1

You will lead engineers working on AI-powered product capabilities, platform services, and ... Help define and enforce best practices for AI system development, MLOps, service reliability ...

Attain Talent

Lead AI/ML Engineer

OR · On-site +1

$180K - $230K/yr

Attain Talent is seeking a Lead AI/ML Engineer to lead the design, evaluation, and implementation ... Experience deploying AI solutions using MLOps best practices. * Experience with containerization ...

LTS

AI Platform and Harness Engineer

OR · On-site +1

LTS is seeking an AI Platform and Harness Engineer to develop and maintain the infrastructure ... Experience implementing LLMOps or MLOps platforms and deployment pipelines. * Experience with AI ...

SentinelOne

Senior Backend Software Engineer, ObservoAI

OR · On-site +1

$122K - $161K/yr

As a Senior Software Engineer, you will be tasked with leading the architectural design and ... and MLOps practices for production ML systems. * Expert knowledge of observability tools and ...

Deloitte

AI Engineer Consultant

Portland, OR · Hybrid

... LLMOps/MLOps capabilities (evaluation, monitoring, governance workflows, model/prompt/version ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

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Mlops Engineer information

What is an MLOps engineer?

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and operations by automating workflows, optimizing infrastructure, and ensuring model reliability. Their role includes CI/CD for ML models, data pipeline management, and performance monitoring. They also work with cloud platforms, containerization, and orchestration tools to scale ML systems efficiently.

What are some common challenges MLOps engineers face in their daily work?

Mlops Engineers often encounter challenges in integrating new machine learning models into existing production systems while ensuring minimal downtime and maintaining data integrity. Managing the scaling and orchestration of models across various cloud or on-prem environments can be complex, requiring close coordination with data scientists and DevOps teams. Staying up to date with rapidly evolving tools and best practices is also essential in this field. Addressing these challenges provides valuable opportunities to innovate and improve both technical processes and team collaboration.

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 strong skills in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science, engineering, or a related field. Familiarity with tools such as Docker, Kubernetes, TensorFlow, AWS/GCP/Azure, and CI/CD systems is essential, and certifications like AWS Certified Machine Learning or Kubernetes Administrator are often valued. Effective communication, problem-solving, and teamwork are crucial soft skills for collaborating across data science and IT teams. These abilities enable Mlops Engineers to efficiently deploy, manage, and scale machine learning models in dynamic production environments.

What do you need to be a MLOps engineer?

To become a MLOps engineer, you typically need a strong background in software engineering, machine learning, and cloud platforms. Proficiency in programming languages like Python, experience with containerization tools such as Docker, and knowledge of CI/CD pipelines are essential. Certifications in cloud services and familiarity with tools like Kubernetes and ML frameworks also enhance qualifications.

Who earns more, ML engineer or MLOps engineer?

MLOps engineers typically earn slightly more than ML engineers due to their focus on deploying, maintaining, and scaling machine learning systems, which requires expertise in cloud platforms, automation, and infrastructure. Salary differences can vary based on experience, location, and company size, but MLOps roles often command higher compensation because of their specialized skill set.

What are the most commonly searched types of Mlops Engineer jobs in Oregon?

The most popular types of Mlops Engineer jobs in Oregon are:

What are popular job titles related to Mlops Engineer jobs in Oregon?

For Mlops Engineer jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Mlops Engineer jobs in Oregon look for?

The top searched job categories for Mlops Engineer jobs in Oregon are:

What cities in Oregon are hiring for Mlops Engineer jobs?

Cities in Oregon with the most Mlops Engineer job openings:

Mlops Engineer jobs near you
Infographic showing various Mlops Engineer job openings in Oregon as of August 2026, with employment types broken down into 88% Full Time, 8% Part Time, and 4% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

Looking for MLOps Engineer in Portland, OR- Hybrid

Saksoft

Portland, OR • On-site

Other

Posted 2 days ago

New

Job description

MLOps Engineer 

Location: Portland, OR- Hybrid

Hybrid: 3 days office

Contract

 JD:

This data science role requires a minimum of 7 years of Python and data science experience, 3 years of AWS experience, and hands-on delivery of machine learning and Generative AI use cases. 

Core Technical Requirements

• Data Science: Minimum 7 years of hands-on coding and model development in Python. 
• Cloud Infrastructure: Minimum 3 years of production experience working within the AWS ecosystem. 
• Machine Learning: Proven background in machine learning with at least 5 distinct, well-documented use cases covering a mix of classification, regression, or forecasting models. 
• Generative AI: Demonstrated delivery of at least 2 Generative AI use cases (Preferred candidates with: keywords such as multimodal applications, image-plus-text processing, or advanced model fine-tuning in GenAI use cases)

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