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Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonPyTorchScikit-LearnTensorflow
About the role
Key responsibilities & impact- Build and automate ML pipelines for training, deployment, monitoring, and retraining.
- Deploy and manage machine learning solutions on cloud platforms, preferably Azure.
- Implement model monitoring, governance, versioning, and performance tracking.
- Collaborate with Data Science and Engineering teams to productionize ML models.
- Manage cloud-based ML services and infrastructure.
- Improve MLOps platforms, tools, and deployment practices.
- Work with stakeholders and technology partners to deliver scalable ML solutions.
Requirements
What you’ll need- 5-8 years of experience in ML Engineering, MLOps, or related roles.
- Strong hands-on experience with Python.
- Experience with TensorFlow, PyTorch, or Scikit-learn.
- Strong understanding of the ML lifecycle and model deployment.
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Experience building automated CI/CD and ML pipelines.
- Knowledge of Docker, Kubernetes, MLflow, or Kubeflow.
- Strong communication and stakeholder management skills.
Benefits
Comp & perks- Competitive compensation and benefits.
- Remote and flexible work environment.
- Opportunity to shape technology strategy and business outcomes.
- Strong learning and leadership growth opportunities.