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Mlops Data Engineer Jobs in Oregon (NOW HIRING)

Tebra

Data Engineer

OR · Remote

$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 ...

Versapay

OR

$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 ...

iHerb

OR · On-site

$114K - $137K/yr

This role will contribute to the company's data-driven culture, bring innovative approaches to cloud-native engineering, and help advance our MLOps capabilities to support production-grade AI/ML ...

ClickHouse

OR

$114K - $137K/yr

... BI, MLOps, or data transformation) * Hands-on experience applying AI/ML in production data ... Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role

Cotiviti

Principal Data Engineer AI

Own the MLOps infrastructure that machine learning engineers depend on, including experiment tracking, model artifact storage, and deployment tooling. * Own data provenance management and maintain ...

Striim, Inc.

OR

$120K - $130K/yr

Working alongside experienced Solution Architects and Engineering teams, this role provides an ... Evaluate emerging technologies across AI, MLOps, cloud computing, and real-time data streaming.

phData

OR

You will collaborate closely with clients, data scientists, data engineers, platform/DevOps teams ... Translate business and data science requirements into scalable technical and MLOps solutions that ...

Liberty Information Technology Limited

... MLOps, and building GenAI solutions to join our Enterprise Data & Data Science team. In this role ... Collaborate with other data scientists, machine learning engineers, data engineers, and business ...

Juvare

Senior DevOps Engineer

OR · On-site +1

$129K - $166K/yr

Experience supporting AI/MLOps workflows is a plus. Location * Atlanta / Remote Must Have * Cloud ... Data & Lifecycle: Understanding of data pipelines and model lifecycle management Nice to Have

Deckers

Sr. Manager, Data & ML Engineering

OR · Remote

... MLOps patterns in partnership with Data Science and analytics teams. We celebrate diversity--of ... Partner with Cloud Engineering and Security to ensure AWS data solutions meet security, privacy ...

Particle41

OR · On-site

You will collaborate with data scientists, data engineers, software engineers and client ... Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker). * Strong ...

Aptive

MLOps and CI/CD automation * Cloud infrastructure and DevOps * Data lifecycle management * Risk and dependency management * Resource planning and forecasting * Executive reporting and stakeholder ...

New York Life

$147K - $211K/yr

Account for any GCP implementation with MLOps and Agentic AI tools in Vertex AI * Enhance ... GCP Professional certifications (Cloud Architect, DevOps Engineer, or Data Engineer). * Experience ...

OneStudyTeam

Senior Machine Learning Engineer

OR · Remote

$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 ...

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All JobsMlops Data Engineer JobsMlops Data Engineer Jobs in Oregon

Mlops Data Engineer information

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

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects as organizations prioritize operationalizing AI solutions.

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

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps Data Engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What are MLOps Data Engineers?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What is the salary of data engineer in MLOps?

The salary of an MLOps Data Engineer typically ranges from $90,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning tools tend to earn higher salaries.

What engineer makes 500,000 a year?

Highly experienced senior MLOps Data Engineers with specialized skills in cloud platforms, automation, and large-scale data processing can earn salaries approaching or exceeding $500,000 annually, especially in competitive tech hubs or large organizations. Such roles often require advanced certifications, extensive experience, and expertise in tools like Kubernetes, Docker, and cloud services like AWS or Azure.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer’s ability to support scalable and reliable ML systems.
What are popular job titles related to Mlops Data Engineer jobs in Oregon? For Mlops Data Engineer jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Mlops Data Engineer jobs in Oregon look for? The top searched job categories for Mlops Data Engineer jobs in Oregon are:
What cities in Oregon are hiring for Mlops Data Engineer jobs? Cities in Oregon with the most Mlops Data Engineer job openings:
Mlops Data Engineer jobs near you

Data Engineer

Tebra

OR • Remote

$114K - $137K/yr

Other

Posted 4 days ago

Job description

About the Role

As a Data Engineer focused on AI/ML, you'll build, maintain, and optimize the data infrastructure that powers Tebra's intelligent features. You'll partner closely with Machine Learning Engineers, Data Scientists, and Software Engineers to transform complex healthcare data into high-quality datasets and real-time features that enable machine learning models.

This is a hands-on engineering role where you'll contribute to scalable data pipelines, improve data quality, and help ensure our AI systems are powered by reliable, performant, and well-governed data. You'll work on modern data platforms and gain experience building solutions that support both model training and production inference.

Your Area of Focus
  • Design, build, and maintain scalable data pipelines for feature extraction, training data generation, and model monitoring.
  • Develop and enhance data systems that support analytics and machine learning workloads, including data lakehouse and feature store technologies.
  • Monitor production data pipelines, identify data quality issues or pipeline failures, and implement improvements to ensure reliability and freshness.
  • Participate in engineering design discussions and contribute to technical decisions around data architecture and pipeline implementation.
  • Build reusable data engineering components, including automated data quality checks, schema validation, and testing frameworks.
  • Translate business requirements into scalable data solutions that enable analytics and machine learning use cases.
  • Optimize SQL queries, Spark workloads, and data processing pipelines to improve performance and scalability.
  • 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 and deliver high-quality solutions within an agile team.
Your Professional Qualifications
  • 3+ years of professional experience in Data Engineering, Software Engineering, or a related field.
  • 2+ years of hands-on experience building and maintaining production data pipelines supporting analytics, reporting, or machine learning workloads.
  • Strong proficiency in Python and SQL with experience developing production-quality data pipelines.
  • Experience with modern data processing technologies such as Spark, Airflow, Kafka, or similar distributed data platforms.
  • Experience working with cloud-based data platforms such as Databricks, Snowflake, Delta Lake, or equivalent lakehouse technologies.
  • Understanding of data modeling, data warehousing, and data governance best practices.
  • Familiarity with machine learning data workflows, including training datasets, feature engineering, and data quality concepts.
  • Experience deploying and supporting production data pipelines with monitoring, testing, and CI/CD practices.
  • Strong problem-solving skills, attention to detail, and the ability to collaborate effectively across engineering and product teams.
  • Excellent communication skills and a desire to continuously learn new technologies and engineering practices.

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