Machine Learning Engineer
13 hours ago
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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