Machine Learning Engineer

Responsibilities: Automate model training, evaluation, and deployment processes in development environments. Build and maintain CI/CD pipelines specifically for model training and evaluation. Ensure proper documentation for all tools, CI/CD pipelines, containers, and reusable code to maintain clarity and ease of use for future team members. Document best practices and guidelines for integrating new models into the workflow. Support continuous model retraining and monitoring efforts in collaboration with data scientists. Extend and maintain available containers that are compatible with different stages of deployment (training, testing, etc.). Work closely with the software team responsible for putting model artifacts into the production pipeline, ensuring a smooth handoff of model outputs. Maintain and continuously refactor reusable training/evaluation code, ensuring modularity and scalability. Design and implement tools to support ML workflows, such as monitoring tools orvisualization aids. Qualifications and Skills: Bachelor’s or Master’s degree in Computer Science, Engineering, or Data Science. 5+ years of experience in machine learning engineering. Proven experience in building, maintaining, and automating CI/CD pipelines formachine learning projects. Experience with model deployment and monitoring using AWS services. Familiarity with cloud-based machine learning workflows and infrastructure. Strong proficiency in Python, Bash, and Shell scripting for automation. Proficient in frameworks like TensorFlow, PyTorch, and Scikit-learn for modeltraining and evaluation. Hands-on experience with AWS services: Amazon S3, Amazon SageMaker, AWSLambda, Amazon ECS/EKS. Experience with Docker for the containerization of ML workloads. Willing to work on US hours for the first 6 months.