Clarivate - MLOps Engineer

Note : If shortlisted, you will be invited for initial rounds on 6th December'25 (Saturday) in Bangalore

About You (Skills & Experience Required) :

- Minimum 2 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 :

- 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.