Job Description
About the position Responsibilities • Design and Implement ML Pipelines: Build and maintain automated CI/CD pipelines for machine learning models, covering data preprocessing, model training, evaluation, and deployment. • Productionize Models: Work closely with data scientists to take models from experimentation to a production-ready state, often involving packaging models into microservices or APIs. • Manage Infrastructure: Provision and manage scalable and secure cloud infrastructure using tools like Docker and Kubernetes to support machine learning workloads. • Optimize Resources: Focus on optimizing the machine learning pipeline for efficiency, scalability, and cost-effectiveness. • Collaborate Cross-Functionally: Work with data scientists, ML engineers, software developers, and IT operations to streamline workflows and improve overall efficiency. • Troubleshoot and Support: Provide technical support and resolve production issues related to model performance, deployment, and infrastructure. Requirements • Must be eighteen years of age or older. • Must be legally permitted to work in the United States. • Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field. • 2-4 years of relevant work experience in an MLOps, DevOps. • Strong programming skills in Python. • Experience with Infrastructure management tools, terraform, Jenkins, Python, Shell, Bash, Helm, Elastic Search, Github actions, Relational or noSQL database technology, cloud computing techniques, CI/CD tools, modern software design patterns, and their respective AI/ML services (e.g., AWS SageMaker, Google AI Platform). • Experience with security frameworks for user and services authorization and authentication. • Experience with creating and executing unit, functional, destructive and performance tests. • Experience with modern debugging and root cause analysis techniques. • Experience with version control system. • Experience with Kubernetes and cloud products. • Experience in networking traffic management. • Deep knowledge of containerization and orchestration tools, including Docker and Kubernetes. • Proven experience with CI/CD tools like Jenkins, GitLab CI, GitHub Actions, or Azure DevOps. • Familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. • Experience with Infrastructure as Code (IaC) tools like Terraform or CloudFormation is highly desirable. • Experience with ML experiment tracking and versioning tools like MLflow or DVC (Data Version Control) is a plus. • Solid understanding of software engineering best practices, including code testing, security, and documentation. • Excellent communication skills with the ability to effectively collaborate with both technical and non-technical teams. Apply tot his job