In this Role, you’ll get to:
- Assist in the design and development of on-premises MLOps solutions under the guidance of senior team members to support the delivery of machine learning models.
- Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training and deployment systems.
- Contribute to the development and maintenance of monitoring and management tools for the on-premises MLOps infrastructure.
- Engage with various stakeholders within the organization to gather insights into their machine learning needs and requirements and observe how MLOps solutions are developed to meet those needs.
- Stay informed about the latest trends and technologies in MLOps, LLMOps, machine learning, and artificial intelligence, with opportunities to learn from experts within the field.
- Receive mentoring from senior members of the team to grow your skills and expertise in MLOps and related areas.
What you’ll Need to Succeed:
- Currently enrolled in or a recent graduate of a degree program in Computer Science, Software Engineering, Data Science, or a related field.
- Strong desire to learn and good communication skills, with an enthusiasm for collaborative problem-solving.
- Basic programming skills in a modern programming language (Java, Scala, Python, Kotlin).
It’s Great if you have:
- Exposure to any MLOps platforms, such as Kubeflow or MLFlow, either through coursework, projects, or internships.
- Familiarity with any Data Analytics or ML frameworks – like numpy, scipy, pandas, scikit-learn, Tensorflow, PyTorch – gained through academic projects or self-learning.
- Some knowledge of Big Data tools – Spark, S3, Hadoop – from classes, projects, or internships.
- Awareness of containerization and container orchestration technologies, such as Docker and Kubernetes, from coursework or hobby projects.
- An understanding of DevOps and CI/CD practices through academic exposure or personal projects.
- Any experience in creating APIs or working with web services is a plus.
- A keen interest in machine learning engineering and a willingness to explore how it can be scaled effectively.
- Curiosity about designing and building MLOps infrastructure components, such as data pipelines, model training systems, and monitoring tools.
