Machine Learning Engineer

We are looking for a ML Ops Engineer to join our Technology team at Clarivate.

You will get the opportunity to work in a cross-cultural work environment while working on the latest web technologies with an emphasis on user-centered design.

About You (Skills & Experience Required)

• Bachelors or masters degree in computer science, Engineering, or a related field.

• 5+ years of experience in machine learning, data engineering, or software development.
• Good experience in building data pipelines, data cleaning, and feature engineering is essential for preparing data for model training.
• Knowledge of programming languages (Python, R), and version control systems (Git) is necessary for building and maintaining MLOps pipelines.
• Experience with MLOps-specific tools and platforms (e.g., Kubeflow, MLflow, Airflow) can streamline MLOps workflows.
• DevOps principles, including CI/CD pipelines, infrastructure as code (IaaC), and monitoring is helpful for automating ML workflows.
• Experience with atleast one of the cloud platforms (AWS, GCP, Azure) and their associated services (e.g., compute, storage, ML platforms) is essential for deploying and scaling ML models.
• Familiarity with container orchestration tools like Kubernetes can help manage and scale ML workloads efficiently.

It would be great if you also had,

• Experience with big data technologies (Hadoop, Spark).
• Knowledge of data governance and security practices.
• Familiarity with DevOps practices and tools.

What will you be doing in this role?

Model Deployment & Monitoring:

• Oversee the deployment of machine learning models into production environments.
• Ensure continuous monitoring and performance tuning of deployed models.
• Implement robust CI/CD pipelines for model updates and rollbacks.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Communicate project status, risks, and opportunities to stakeholders.
• Provide technical guidance and support to team members.

Infrastructure & Automation:

• Design and manage scalable infrastructure for model training and deployment.
• Automate repetitive tasks to improve efficiency and reduce errors.
• Ensure the infrastructure meets security and compliance standards.

Innovation & Improvement:

• Stay updated with the latest trends and technologies in MLOps.
• Identify opportunities for process improvements and implement them.
• Drive innovation within the team to enhance the MLOps capabilities.