... systems, and apply MLOps practices for versioning, orchestration, monitoring, and CI/CD ... internship, personal, or professional projects. - Strong Python foundation and hands-on experience ...
... systems, and apply MLOps practices for versioning, orchestration, monitoring, and CI/CD ... internship, personal, or professional projects. - Strong Python foundation and hands-on experience ...
Mlops Engineer Internship information
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 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 is the difference between Mlops Engineer Internship vs Data Engineer Internship?
| Aspect | Mlops Engineer Internship | Data Engineer Internship |
|---|---|---|
| Required Credentials | Basic knowledge of machine learning, cloud platforms, scripting | Strong SQL, programming, data modeling skills |
| Work Environment | Tech companies, startups, cloud service providers | Data-centric teams, analytics firms, tech companies |
| Industry Usage | AI/ML projects, deployment pipelines | Data pipelines, database management |
| Search & Comparison Intent | Understanding roles in ML deployment | Understanding 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 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 the most commonly searched types of Mlops Engineer jobs in Michigan?
The most popular types of Mlops Engineer jobs in Michigan are:
What are popular job titles related to Mlops Engineer Internship jobs in Michigan?
For Mlops Engineer Internship jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Mlops Engineer Internship jobs in Michigan look for?
The top searched job categories for Mlops Engineer Internship jobs in Michigan are:
What cities in Michigan are hiring for Mlops Engineer Internship jobs?
Cities in Michigan with the most Mlops Engineer Internship job openings:
Corning rating
8.2
Based on 128 frontline employees who took The Breakroom Quiz
88th of 540 rated manufacturers
Job description
Are you ready to turn machine learning ideas into reliable solutions that improve how products are made?
What is your role?
As a Machine Learning Engineer, you will work within a collaborative technical team to build, deploy, monitor, and maintain machine learning solutions that create measurable business value. You will partner with data scientists, analytics leaders, IT, and manufacturing teams to move models from experimentation into scalable and reliable production environments. This role is based in Monterrey and requires regular onsite presence, with hybrid flexibility.
Major responsibilities and tasks of the position:
- Participate in the development and maintenance of end-to-end machine learning pipelines, including data ingestion, preprocessing, training, validation, deployment, monitoring, and retraining.
- Collaborate with data scientists and engineers to translate prototypes and experimental models into production-ready solutions.
- Support model serving through APIs, batch jobs, or real-time systems, and apply MLOps practices for versioning, orchestration, monitoring, and CI/CD.
- Troubleshoot data, model, deployment, and integration issues while maintaining clear technical documentation and participating in code reviews.
What do you need to have?
- Bachelor's degree in Computer Science, Engineering, Data Science, Software Engineering, Data Engineering, or a related technical field.
- 0-2 years of experience in machine learning, data science, data engineering, software engineering, or relevant hands-on academic, internship, personal, or professional projects.
- Strong Python foundation and hands-on experience with at least one machine learning library or framework such as scikit-learn, TensorFlow, or PyTorch.
- Understanding of the machine learning lifecycle and the ability to clearly explain a project, your personal contribution, the tools used, and the outcome.
- Basic familiarity with data pipelines, databases, APIs, software development practices, or workflow automation.
- Advanced technical and business English, both written and verbal.
- Ability to work onsite in Monterrey at least two days per week and support plant-based projects as needed.
What would be a plus?
- Exposure to Databricks, MLflow, Kubeflow, Docker, Kubernetes, CI/CD, or other MLOps/DevOps tools.
- Experience with manufacturing, industrial, process, plant, or production data.- A GitHub portfolio or other examples that demonstrate hands-on technical work.
What do we offer?
- Competitive benefits above the requirements of Mexican law.
- Opportunity to work on high-impact machine learning initiatives that support manufacturing and business transformation.
- Collaborative global environment with exposure to Data Science, IT, analytics, and manufacturing teams.
- Learning and career development in a growing technical organization.
Corning is committed to providing equal employment opportunities and considers requests for reasonable accommodations in accordance with applicable laws. Individuals with disabilities or sincerely held religious beliefs may request reasonable accommodation to participate in the application or interview process, perform essential job functions, or access other benefits and privileges of employment. To submit a request for reasonable accommodations related to disability or religion, please contact us at accommodations@corning.com