Description
WHAT AM I GOING TO DO?
Design and implement end-to-end MLOps pipelines for deploying and managing ML models.
Automate workflows for model training, testing, and deployment.
Optimize and manage cloud infrastructure for AI/ML workloads, including AWS, GCP, and Azure.
Monitor model performance and ensure system reliability in production environments.
Collaborate with data scientists and engineers to operationalize ML models.
Establish best practices for model versioning, reproducibility, and governance.
Develop tools and frameworks to streamline the ML lifecycle.
Utilize GPUs for model training and optimization to enhance performance.
Proactively identify and resolve obstacles, demonstrating initiative and a solution-oriented mindset.
Requirements
REQUIREMENTS
4+ years of experience in MLOps, DevOps, or related fields.
Strong proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
Expertise in CI/CD pipelines and infrastructure-as-code tools (e.g., Terraform).
Knowledge of monitoring tools and practices for production ML systems.
Experience with cloud platforms (AWS, GCP, and Azure) for ML deployment.
Solid understanding of data preprocessing, feature engineering, and model training workflows.
Proven experience working with GPUs for deep learning and model training.
Excellent communication and teamwork abilities.
Nice to have:
Familiarity with data governance and lineage tools.
Knowledge of advanced ML topics such as reinforcement learning or federated learning.
Experience integrating ML models with APIs and real-time systems.
Level 3 Civilian Clearance