MLOps Data Engineer

As an MLOps Data Engineer at CGI, you'll be responsible for deploying data infrastructure that supports machine learning workflows, both in cloud and on-premises. You'll integrate storage, processing, and feature management systems to ensure seamless data flow, optimizing existing data pipelines to boost performance. Collaborating with diverse teams ensures that you align technical solutions with business goals, while also applying best practices in data governance and security.

To be successful as an MLOps Data Engineer at CGI, candidates should have advanced Python programming skills and proven experience in machine learning data workflows. Familiarity with cloud platforms (AWS, GCP, Azure), strong knowledge of data technologies (like Hadoop and Spark), and experience with CI/CD pipelines are also crucial. A Bachelor's degree in computer science or a related field is required.

CGI is committed to the growth of its employees, including MLOps Data Engineers. From day one, you'll have access to learning opportunities, mentorship, and tuition assistance that helps you deepen your skills. The company's collaborative culture encourages sharing knowledge, while providing you with the tools to innovate and expand your expertise.

MLOps Data Engineers at CGI work with a wide range of technologies, including Python, Apache Spark, Kafka, as well as containerization tools like Docker and Kubernetes. You'll also engage with MLOps-specific frameworks such as Kubeflow and MLflow, ensuring you have the skills to manage data models and automate testing processes effectively.

The work environment for MLOps Data Engineers at CGI is collaborative, inclusive, and dynamic. You'll be part of a team that values ownership and teamwork, allowing you to engage in meaningful projects that impact the financial services industry. With options for flexible locations such as Pittsburgh or Cleveland, you can expect a culture that promotes respect and belonging while you contribute to innovative solutions.

In answering this question, focus on specific projects where you have deployed ML workflows. Discuss the technologies you used, such as Apache Spark or cloud services, and the impact of your work on the project’s success. Highlight your role in optimizing data pipelines and ensuring seamless integration.

When discussing data privacy and security, emphasize your knowledge of industry standards and best practices. Mention specific protocols and tools you have used to protect sensitive data, along with your experience in ensuring compliance with regulatory standards in financial services.

Focus on your analytical approach to identify bottlenecks in data pipelines. Discuss tools and methods you've employed to optimize performance, such as refactoring code or implementing caching solutions, and how these improvements contributed to overall workflow efficiency.

Share a specific engineering challenge that highlights your problem-solving skills. Describe the situation, the steps you took to address it, and the solution's results. This showcases your technical acumen and ability to navigate complex issues effectively.

Discuss your experience with MLOps tools such as Kubeflow or MLflow. Explain how you have used them for model deployment, tracking, or management, and the advantages they provided for your projects, illustrating your capability to integrate modern solutions into your workflows.

Highlight your collaboration techniques with cross-functional teams. Discuss specific instances of team projects, communication methods, and how you ensured that technical solutions aligned with business objectives, reinforcing the importance of teamwork in delivering successful outcomes.

Explain your involvement with Continuous Integration and Continuous Deployment pipelines, including the technologies you have used like Jenkins. Describe a project where CI/CD facilitated smoother deployments, illustrating the benefits this brought to the development process.

Provide an overview of the concept of feature stores and their role in managing features for machine learning models. Discuss your experience implementing them, such as Hopsworks, and how they help streamline the machine learning pipeline and improve model performance.

Share your strategies for keeping current with data engineering trends, like attending conferences, participating in online courses, or following relevant blogs and forums. This signals your commitment to continuous learning and adapting to emerging technologies.

Discuss your proficiency with programming languages relevant to MLOps, specifically Python, Java, or Scala. Provide examples of how you've utilized these languages in projects, showcasing your coding skills and ability to develop efficient data processing solutions.