MLOps Engineer

Job Description

• Take ownership of the ML deployment pipeline.
• Design and implement deployment strategies for various tools and ML models with consideration of scalability, cost, and ease of use.
• Ensure the security of proprietary work and manage access policies to our IP.
• Automate testing, enforce code quality, and apply development best practices.
• Collaborate with the team and be proactive with improvements.
• Be open to learning and open to teaching.

Basic Qualifications:

• Experience in MLOps or a similar role, with proven experience in deploying machine learning models to production.
• Experience in designing, building, and managing MLOps pipelines.
• Experience in cloud computing, particularly with AWS.
• Experience inbuilding and managing API endpoints.
• Experience with MLOps tools such as MLFlow, KubeFlow, or AirFlow.
• Familiarity with ML tools and frameworks such as pandas, Numpy, Scikit-learn, and PyTorch.
• Experience with Infrastructure as Code tools such as Cloud Formation or Terraform.
• Strong problem-solving skills and the ability to work independently or collaboratively.

Nice to Have

• Strong experience with container management and deployment, such as using Kubernetes, AWS ECR, AWS Fargate, AWS Batch.
• Prior experience in building an end-to-end MLOps pipeline for deep learning models from the ground up.