MLOps & Data Engineer - JB-646
Posted 20 days ago · 43 applicants
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The role in plain words
This role involves designing and implementing end-to-end MLOps pipelines to deploy and manage machine learning models. You will automate workflows for model training, testing, and deployment, while optimizing cloud infrastructure across AWS, GCP, and Azure. Additionally, you will monitor model performance in production and collaborate with data scientists to operationalize models.
- 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
- 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
Extracted from the job description · kept up to date automatically
Who this suits
This role suits professionals with at least 4 years of experience in MLOps or DevOps who have strong Python skills and hands-on experience with Docker, Kubernetes, and GPUs. It is less ideal for those without experience in cloud platforms or those who do not possess or cannot obtain a Level 3 Civilian Clearance.
Full job description
Original listing · kept for referenceWHAT 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
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
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- 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