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Mlops Data Engineer Jobs in Oregon (NOW HIRING)

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

$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

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.

New

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

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

Senior Data Engineer

Versapay

OR

$105K - $143K/yr

Full-time

Posted 8 days ago

Job description

About Versapay 

Versapay turns accounts receivable (AR) into a competitive advantage.

Inefficient AR processes slow cash flow and stall growth. Versapay removes friction, unlocks working capital, and accelerates momentum - giving finance leaders the clarity and control they need to drive business forward.

Versapay automates accounts receivable, removing barriers to collecting and reconciling B2B payments. Our solutions connect finance teams, customers, and business systems in one ecosystem to ensure cash flow clarity. With over 10,000 customers and 5M+ companies transacting on the platform, Versapay processes over 110M transactions and $257B annually.

Think you might be the next Veep to join? Read on!!




Here's how you'll make a huge impact here - and on your career: 

The Analytics team is evolving our enterprise capabilities from foundational governance into a robust data platform, safely accelerating strategic AI enablement and delivering high-margin commercial data products. As a Senior Data Engineer, you will be pivotal in optimizing and scaling our foundational Snowflake architecture while aggressively pushing toward agentic engineering and machine learning operations. You will operate as a full-stack generalist within the engineering pod, sharing cross-functional responsibility for pipeline resilience, advanced observability, and the deployment of intelligent semantic models that directly feed our product ecosystem. 

Reports To: Manager of Data Engineering 

 
What You'll Do:

  • Architect for the Future: Optimize our existing Snowflake architecture, establishing strict environmental isolation and scalable structures that prepare our data for eventual downstream commercialization and product offerings. 

  • Drive Agentic Engineering: Leverage tools like Snowflake Cortex, Cursor, and UiPath to automate workflows, build semantic models, and deploy agents that accelerate time-to-value. 

  • Establish Data Observability: Implement and manage robust data quality and observability frameworks to ensure pipeline reliability and proactive issue resolution. 

  • Operationalize Machine Learning: Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle management of ML models directly within Snowflake. 

  • Execute Shared Ownership: Partner closely with your peers under the Data Engineering Manager to share responsibilities across pipeline management, MLOps, and architecture, avoiding siloed knowledge and ensuring comprehensive team coverage. 

  • Model for Enterprise Utility: Synthesize disparate operational entities into a unified, enterprise-wide semantic model that supports both internal analytics and future data monetization efforts. 

Qualifications

  • 5+ years of Data Engineering experience with a deep, specialized focus on Snowflake's advanced features (e.g., RBAC, materialized views, dynamic tables, Snowpipe, stored procedures). 

  • Advanced proficiency in SQL and Python, with a strong foundation in applying software engineering best practices to ELT processes. 

  • Observability Expertise: Hands-on experience implementing data observability and monitoring platforms (such as DataDog) to manage data quality at scale. 

  • AI & MLOps Exposure: Demonstrated experience using AI-assisted development tools (e.g., Cursor, Cortex) and familiarity with MLOps principles for productionalizing machine learning models. 

  • Pipeline Management: Experience building and maintaining resilient, low-touch data pipelines using modern integration and orchestration tools (e.g., Fivetran, AWS Glue, AWS Lambda). 

What You'll Bring To The Team:
  • Technical Competency: Advanced SQL skills, proficiency with Python/R, and experience with BI tools. Focus on self-sufficiency and leveraging AI tools to accelerate development. 
  • "Builder" Mentality: An ability to thrive in fast-paced environments with a track record of defining and executing high-impact initiatives. A desire to solve complex problems, remediate technical debt, and find creative solutions for scaling our platform. 
  • Business Acumen: Strong business acumen with a proven ability to translate complex data analysis into strategic recommendations. Adept at identifying key drivers and influencing decision-making. You understand the business behind the data and the path to commercialization. 
  • Empathetic Collaboration: Assertive with humility - able to communicate both persuasively and positively. Maintain high standards for verbal and written communication while seamlessly sharing domain responsibilities across the engineering pod. 
  • Trusted Advisor: Possesses a high degree of integrity, the relentless pursuit of truth, and an ability to inspire change, particularly in championing data quality and observability standards. 
What Will Make You Stand Out:

  • Deep domain expertise navigating complex merchant payment ecosystems (e.g., Adyen), operating under rigorous enterprise data governance and security standards. 

  • Proven ability to architect the translation of high-velocity transactional events into highly optimized, columnar analytical architectures. 

  • Direct experience architecting data products for commercialization, external endpoints, or embedded analytics within a SaaS platform.

$110,000 - $140,000 a year
#LI-Remote

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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