The ML Ops Engineer will be part of Clarivate's Technology team, working in a cross-cultural environment to deploy, monitor, and scale machine learning models. The role focuses on building robust MLOps pipelines, automating workflows, and managing scalable infrastructure.

About You (Skills & Experience Required) :

- Education : Bachelor's/master's in computer science, Engineering, or related field.

- Experience : 3+ years in ML, data engineering, or software development.

Technical Skills :

- Data pipelines, cleaning, feature engineering, Python programing language with R and Git

- MLOps tools : Kubeflow, MLflow, Airflow

- CI/CD, Infrastructure as Code (IaaC)

- Cloud platforms : AWS, GCP, Azure

- Kubernetes for container orchestration

It would be great if you also had :

- Experience in Big data : Hadoop, Spark

- Data governance and security

- DevOps practices

What will you be doing in this role?

- Model Deployment & Monitoring : Deploy ML models, monitor performance, implement CI/CD for updates.

- Infrastructure & Automation : Design scalable infrastructure, automate tasks, ensure security and compliance.

- Innovation & Improvement : Stay updated on MLOps trends, drive process improvements, and foster innovation.

- Collaboration : Work with cross-functional teams to translate business needs into technical solutions

About the Team :

You would be part of our incredible data science team of Intellectual property (IP) group & work closely with product and technology teams spreads across various locations worldwide. You would be working on interesting IP data and interesting challenges to create insights and drive business acumen to add value to our world class products and services.

Hours of Work :

This is a permanent position with Clarivate.9 hours per day including lunch break. you should be flexible with working hours to align with globally distributed teams and stakeholders.