Machine Learning Engineer

3 days ago

Noida, Uttar Pradesh, India Clarivate Full time ₹ 20,00,000 - ₹ 25,00,000 per year

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.

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