MLOps Engineer

Job Description
• Deploy and manage machine learning models in production environments, ensuring high availability and performance.
• Automate the deployment process using CI/CD pipelines, ensuring that models are deployed efficiently and reliably.
• Develop and maintain scripts and tools for automating model training, deployment, and monitoring.
• Monitor the performance of deployed models, identifying and resolving issues proactively.
• Implement monitoring tools and dashboards to track model performance metrics, detect anomalies, and ensure scalability.
• Perform regular maintenance tasks, including model retraining, updates, and scaling to handle increased data loads.
• Work closely with data scientists to understand model requirements and ensure smooth integration into production systems.
• Collaborate with software engineers to integrate machine learning models into existing applications and services.
• Partner with data engineers to build and maintain data pipelines that feed into machine learning models.
• Manage and optimize the infrastructure required for machine learning operations, including cloud platforms, Kubernetes clusters, and containerized environments.
• Ensure that the infrastructure is scalable, secure, and cost-effective.
• Implement best practices for infrastructure as code (IaC) and manage configuration using tools like Terraform or Ansible.
• Apply DevOps principles to machine learning operations, including continuous integration, continuous delivery, and automated testing.
• Use version control systems (i.e., Git) to manage code and model versioning.
• Implement and manage containerization using Docker and orchestration using Kubernetes.
• Troubleshoot and resolve issues related to model deployment, infrastructure, and performance.
• Optimize model deployment processes to reduce latency and improve scalability.
• Continuously evaluate and implement new tools and technologies to improve ML Ops practices.

Education
• Bachelor's degree in Computer Science, Information Technology, or a related field.

Technical Skills
• Proficiency in machine learning techniques and model deployment, with experience in automating these processes.
• Strong programming skills in Python, Java, or other relevant languages.
• Experience with DevOps tools such as Jenkins, Git, Terraform, or Ansible.
• Expertise in containerization (Docker) and orchestration (Kubernetes).
• Experience with cloud platforms (i.e., AWS, GCP, Azure) for deploying and managing machine learning models.
• Knowledge of data engineering concepts, including data pipelines and ETL processes

(ref:hirist.tech)