Clarivate - MLOps Engineer

4 weeks ago


Bengaluru, India hirist.tech Full time

Note : If shortlisted, you will be invited for initial rounds on 6th December'25 (Saturday) in You (Skills & Experience Required) :- Bachelors or masters degree in computer science, Engineering, or a related field.- 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 ? 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. (ref:hirist.tech)



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