Senior Machine Learning Operations Engineer

Senior Machine Learning Operations Engineer

Requirements

• 5+ years of experience in the IT Industry, with at least 2 years specifically focused on Machine Learning, showcasing a strong grasp of machine learning principles alongside data engineering concepts.
• Strong programming skills in languages such as Python or R, along with experience in using machine learning libraries and frameworks like TensorFlow, PyTorch, or Scikit-learn for model development and evaluation.
• Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning, to effectively implement and optimize models in production environments.
• Proficiency in cloud platforms such as Azure or Google Cloud Platform (GCP), with hands-on experience in utilizing their machine learning services and tools for model deployment and management.
• Experience with orchestration tools like Kubeflow and Apache Airflow for managing machine learning workflows and data pipelines.
• Experience with containerization technologies like Docker and orchestration tools such as Kubernetes, enabling efficient deployment, scaling, and management of machine learning applications.
• Knowledge of CI/CD practices and tools, including Jenkins, GitLab or GitHub to automate the deployment and testing of machine learning models, ensuring rapid and reliable updates.
• Understanding of monitoring and logging tools (e.g., Prometheus, or Grafana) to track model performance and system health, allowing for proactive management and troubleshooting.
• Familiarity with data processing frameworks such as Pandas, Dask, and Spark, which are essential for handling large datasets and performing distributed data processing in cloud environments.
• Excellent problem-solving skills and the ability to work collaboratively in a team environment, adapting to new challenges and technologies as the field of machine learning continues to evolve.

Responsibilities

• Partner closely with data scientists to understand model specifications, driving a seamless transition for model launch from development to production.
• Create and manage continuous integration and continuous deployment (CI/CD) pipelines for machine learning models to automate the deployment process.
• Oversee the end-to-end lifecycle of machine learning models, including development, validation, deployment, monitoring, and retraining.
• Track model performance using relevant metrics and techniques, such as accuracy, and precision score, to ensure alignment with business objectives and address issues related to model drift and data quality.
• Conduct performance analysis and optimization through techniques like hyperparameter tuning and resource allocation strategies.
• Create and maintain automated workflows for model training and inference to enhance efficiency.
• Optimize the underlying infrastructure for machine learning operations, including cloud services and container orchestration, to ensure scalability and performance.
• Establish version control practices for models, datasets, and code to ensure reproducibility and facilitate collaboration.
• Conduct code reviews and contribute to the development of engineering frameworks and best practices for model deployment and maintenance.
• Develop and maintain comprehensive documentation for model deployment processes, architecture, and workflows.
• Perform root cause analysis for model failures and implement corrective actions to improve reliability.
• Offer support for troubleshooting and resolving issues related to model performance and infrastructure.
• Oversee and optimize cloud resources for cost-effective model training and deployment.
• Ensure adherence to security best practices and data privacy regulations in all machine learning operations.
• Mentor and guide junior engineers, providing support in their professional development and helping them enhance their technical skills.

Preferred Qualifications

• Master’s Degree preferred.