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.
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