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Mlops Data Engineer Jobs in Oregon (NOW HIRING)

The Avian Consulting LLC

MLOps Engineer

Portland, OR · On-site

MLOps Engineer Location: Portland, OR (5 days Onsite), they may ask for F2F client interview. We ... You will work closely with data scientists, software engineers, and cloud teams to operationalize ...

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 ...

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 ...

Cotiviti

Principal Data Engineer AI

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 ...

Deloitte

AI Engineer Consultant

Portland, OR · Hybrid

... and HR data domains. * 2+ years of experience operationalizing LLMOps/MLOps capabilities ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

Deloitte

Lead Forward Deployed Engineer - AWS

Portland, OR · On-site

$108K - $143K/yr

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

Deloitte

Senior Forward Deployed Engineer- AWS

Portland, OR

$110K - $152K/yr

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

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 ...

Particle41

AI Engineer

OR · On-site +1

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

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 ...

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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 Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining machine learning models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

What are the key skills and qualifications needed to thrive as an MLOps data engineer?

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 is an MLOps data engineer?

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 MLOps Data Engineer?

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 advanced skills in cloud platforms, automation, and machine learning tools may earn higher compensation.

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

MLOps Engineer

The Avian Consulting LLC

Portland, OR • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Title: MLOps Engineer
Location: Portland, OR (5 days Onsite), they may ask for F2F client interview.

Job Description

We are seeking an experienced MLOps Engineer to join our team onsite in Portland, OR. The ideal candidate will be responsible for designing, deploying, automating, and maintaining machine learning pipelines and infrastructure. You will work closely with data scientists, software engineers, and cloud teams to operationalize ML models and ensure scalable, secure, and reliable AI/ML solutions.

Key Responsibilities

  • Design, build, and maintain end-to-end MLOps pipelines for model training, testing, deployment, and monitoring.
  • Automate ML workflows using CI/CD best practices.
  • Deploy and manage machine learning models in production environments.
  • Develop scalable data and model pipelines on cloud platforms.
  • Monitor model performance, data drift, and system health.
  • Collaborate with data scientists to productionize ML models.
  • Implement model versioning, experiment tracking, and artifact management.
  • Optimize infrastructure for performance, scalability, and cost efficiency.
  • Ensure security, governance, and compliance for ML platforms.
  • Troubleshoot production issues and improve operational reliability.

Required Skills

  • 5+ years of experience in DevOps, Data Engineering, or MLOps.
  • Strong experience with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
  • Experience with containerization technologies like Docker and Kubernetes.
  • Strong knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
  • Experience with cloud platforms (AWS, Azure, or Google Cloud Platform).
  • Experience with Infrastructure as Code tools such as Terraform or CloudFormation.
  • Knowledge of model monitoring, logging, and observability tools.
  • Strong understanding of Git version control and software development best practices.
  • Experience with Linux environments and shell scripting.

 

 

Preferred Qualifications

  • Experience with Generative AI, LLM deployment, or RAG-based applications.
  • Familiarity with Apache Airflow, Kafka, or Spark.
  • Knowledge of feature stores and model registries.

Apply