Machine Learning - Ops

Key Skills

AWS, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Employee life cycle, Machine learning techniques, MLOps tools, Optimization, Python Programming, PyTorch, TensorFlow

Job Description

We are seeking a highly skilled and experienced ML Ops Resource to join our team. As an ML Ops Resource, you will play a crucial role in deploying and maintaining machine learning models in production environments. Your primary focus will be on automating, optimizing, and streamlining the end-to-end ML lifecycle, from model development to deployment and monitoring.

Responsibilities:

• Collaborate with cross-functional teams including data scientists, engineers, and IT professionals to ensure seamless integration and deployment of machine learning models into production environments.
• Build and maintain scalable ML infrastructure, including model versioning, deployment pipelines, and monitoring systems, to enable efficient and reliable model deployment.
• Automate and streamline the ML lifecycle processes, including data preprocessing, feature engineering, model training, validation, and evaluation.
• Implement and maintain CI/CD (Continuous Integration/Continuous Deployment) pipelines for ML models, ensuring smooth and efficient deployments.
• Develop and implement model monitoring and alerting systems to detect and address performance issues, data drift, and model degradation.
• Collaborate with data scientists and engineers to optimize model performance, scalability, and reliability in production environments.
• Conduct performance profiling and optimization to enhance the efficiency and speed of ML model inference and prediction.
• Stay up-to-date with the latest ML Ops tools, technologies, and best practices, and propose innovative solutions to improve the ML deployment process.

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Solid experience (3-5 years) in ML Ops, deploying and maintaining machine learning models in production environments.
• Proficiency in programming languages such as Python and experience with ML frameworks like TensorFlow or PyTorch.
• Strong understanding of AWS cloud platforms and experience with their ML services and infrastructure.
• Experience with containerization technologies like Docker and container orchestration tools like Kubernetes.
• Knowledge of CI/CD pipelines, version control systems (e.g., Git), and automation tools (e.g., Jenkins) for ML model deployment.
• Familiarity with monitoring and logging tools for ML model performance tracking and issue detection.
• Strong problem-solving and troubleshooting skills, with the ability to diagnose and resolve issues related to ML model deployment and performance.
• Excellent communication and collaboration skills to work effectively with cross-functional teams and stakeholders.
• This is an exciting opportunity for an experienced ML Ops professional to contribute to the successful deployment and management of machine learning models in a dynamic and data-driven environment.

About S&P Global Commodity Insights:

At S&P Global Commodity Insights, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value.

We’re a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Commodity Insights coverage includes oil and gas, power, chemicals, metals, agriculture and shipping.

Commodity Insights counts over 4,300 people in more than 30 offices worldwide.