MLOps Engineer

Key Skills

Analytical and Problem solving, AWS, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, DevOps, Git, Google Cloud Platform (GCP), Keras software library, Machine learning techniques, MLOps tools, Python Programming, PyTorch, Rust, Scikit-learn, TensorFlow

Job Description

MLOps is a set of management techniques for the deep learning or production ML lifecycle, formed from machine learning or ML and operations or Ops. These include ML and DevOps methods, as well as data engineering processes for deploying and maintaining machine learning models in production.

Key Responsibilities

• Design, build, and maintain scalable infrastructure for deploying machine learning models into production environments.

• Collaborate closely with data scientists, machine learning engineers, and software engineers to streamline the model deployment process and optimize performance.

• Implement CI/CD pipelines and automation tools to facilitate seamless model training, testing, deployment, and monitoring.

• Develop and maintain monitoring, logging, and alerting systems to ensure the health and performance of deployed machine learning models.

• Implement best practices for version control, model versioning, and reproducibility in machine learning workflows.

• Optimize and fine-tune infrastructure for performance, scalability, and cost efficiency in cloud-based environments.

• Stay updated with the latest advancements in ML Ops, identifying and integrating new tools and technologies to improve processes and workflows.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

• 2-4 years of experience in ML Ops, DevOps, or a related field, with a focus on deploying and managing machine learning models in production.

• Demonstrate a strong proficiency in Python or RUST, using relevant libraries and frameworks for machine learning and deep learning, such as TensorFlow, PyTorch, scikit-learn, and Keras.

• Proficiency in cloud platforms like AWS, Azure, or GCP, and experience with infrastructure as code tools.

• Strong understanding of containerization technologies (e.g., Docker, Kubernetes) and orchestration tools for managing distributed systems.

• Hands-on experience with CI/CD pipelines, version control systems (e.g., Git), and automation tools.

• Hands-on experience with ML Frameworks like pytorch, tensorflow

• Knowledge of machine learning concepts, frameworks, and model deployment techniques.

• Excellent problem-solving skills and the ability to troubleshoot complex issues in production environments.