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Mlops Engineer Internship Jobs in Oregon (NOW HIRING)

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Mlops Engineer Internship information

What is an MLOps engineer internship?

An MLOps Engineer Internship is a temporary position designed for students or recent graduates to gain hands-on experience in the field of Machine Learning Operations (MLOps). Interns typically work alongside experienced engineers to help streamline and automate the process of deploying, monitoring, and maintaining machine learning models in production environments. The internship provides valuable exposure to tools and practices such as CI/CD for ML, containerization, model versioning, and cloud platforms. This role is ideal for those looking to bridge the gap between data science and software engineering, gaining practical skills in both areas. Interns often contribute to real-world projects and learn about best practices in scaling and operationalizing AI solutions.

What are some typical projects or tasks I might work on during an MLOps engineer internship?

As an MLOps Engineer Intern, you can expect to work on tasks such as automating machine learning model deployment pipelines, setting up continuous integration/continuous deployment (CI/CD) workflows, and monitoring models in production. You may also assist with optimizing infrastructure for machine learning workloads, ensuring reproducibility of experiments, and collaborating closely with data scientists and software engineers. These projects are designed to give you hands-on experience with real-world MLOps tools and practices, preparing you for a full-time role in the field.

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

To thrive as an MLOps Engineer Intern, a strong foundation in machine learning concepts, programming (Python, Bash), and familiarity with cloud platforms is essential, often backed by studies in computer science or a related field. Experience with tools such as Docker, Kubernetes, CI/CD pipelines, and version control systems like Git is typically required. Strong problem-solving skills, collaboration, and adaptability help interns navigate technical challenges and team environments. These skills and qualities are crucial for efficiently deploying, maintaining, and scaling machine learning models in production settings.

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

AspectMlops Engineer InternshipData Engineer Internship
Required CredentialsBasic knowledge of machine learning, cloud platforms, scriptingStrong SQL, programming, data modeling skills
Work EnvironmentTech companies, startups, cloud service providersData-centric teams, analytics firms, tech companies
Industry UsageAI/ML projects, deployment pipelinesData pipelines, database management
Search & Comparison IntentUnderstanding roles in ML deploymentUnderstanding data infrastructure roles

The comparison between Mlops Engineer Internship and Data Engineer Internship highlights that both roles involve working with data and cloud technologies but focus on different aspects. Mlops internships emphasize deploying and maintaining machine learning models, while Data Engineer internships focus on building data pipelines and infrastructure. Candidates should choose based on their interest in ML deployment versus data management.

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

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

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

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

Mlops Engineer Internship jobs near you

Junior Solutions Architect - MLOps & Real-Time Data Integration

Striim, Inc.

OR • On-site, Remote

$120K - $130K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 28 days ago

Job description

We are seeking a Junior Solution Architect with a strong foundation in data science, MLOps, cloud data platforms, and modern data engineering to help design and implement real-time data integration and AI-enabled architectures. Working alongside experienced Solution Architects and Engineering teams, this role provides an opportunity for an early-career professional to gain hands-on experience designing scalable streaming data solutions that power enterprise AI, cloud modernization, and real-time analytics.

The ideal candidate is eager to apply data science and machine learning concepts to real-world enterprise challenges, expand technical expertise across modern cloud and data technologies, and develop into a trusted technical architect within a collaborative, fast-paced environment.

Responsibilities

  • Design and implement scalable real-time data integration and Change Data Capture (CDC) solutions using the Striim platform.
  • Design streaming data architectures connecting enterprise databases, cloud data platforms, messaging systems, and AI/ML environments.
  • Develop data pipelines that support machine learning workflows, feature engineering, model inference, and real-time AI applications.
  • Build proof-of-concepts, reference architectures, and deployment patterns for enterprise implementations.
  • Configure, optimize, and troubleshoot data pipelines across cloud and hybrid environments.
  • Collaborate with Engineering, Product, and GTM Engineering teams to validate architectural designs, resolve complex technical challenges, and improve platform capabilities.
  • Participate in architecture reviews, implementation planning, and production readiness activities.
  • Create technical documentation, architecture diagrams, and implementation best practices.
  • Evaluate emerging technologies across AI, MLOps, cloud computing, and real-time data streaming.

Requirements

  • 1-3 years of professional experience or equivalent graduate research, internships, or project experience in data science, machine learning, data engineering, cloud engineering, or solution architecture.
  • Strong foundation in data science, including machine learning algorithms, model selection, feature engineering, and the machine learning lifecycle.
  • Understanding of modern MLOps practices, including model deployment, inference, monitoring, versioning, and CI/CD for machine learning applications.
  • Experience or academic exposure to machine learning frameworks and platforms such as MLflow, Kubeflow, Vertex AI, SageMaker, or Azure Machine Learning.
  • Familiarity with modern data integration concepts, including Change Data Capture (CDC), event-driven architectures, and real-time streaming data pipelines.
  • Working knowledge of relational and NoSQL databases, including SQL proficiency and database administration fundamentals.
  • Experience with cloud platforms and modern cloud data ecosystems, including AWS, Azure, GCP, Databricks, Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
  • Experience programming in Python or Java and working with REST APIs and JSON.
  • Understanding of Docker containers and modern DevOps concepts; familiarity with Kubernetes, Git, and CI/CD pipelines.
  • Strong analytical, troubleshooting, written, and verbal communication skills.
  • Demonstrated curiosity, adaptability, and a passion for learning emerging technologies in AI, cloud computing, and real-time data streaming.
  • Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, Information Systems, or a related technical discipline.

Benefits

  • Competitive salary and pre-IPO stock options
  • Comprehensive health care plans (medical, dental and vision), including medical and dependent FSA
  • Paid Time Off (Vacation, Sick & Public Holidays)
  • The chance to contribute to and shape an upbeat, fully engaged culture

Compensation

$120,000 - $130,000 USD on an annualized basis. In addition to base pay, this role offers the opportunity to earn commission-based rewards.

Applications will be reviewed on a rolling basis and accepted until the position is filled.

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