About the role
Requirements:
- Bachelor's degree or advanced degree or equivalent experience in Computer Science, Software Engineering or a related technical field.
- Strong ability to design and implement cloud architectures for end-to-end ML workflows on AWS.
- Hands-on experience with MLOps frameworks like MLflow, Kubeflow, Airflow, or similar.
- Proficiency with Docker, Kubernetes (EKS/GKE/AKS), and enterprise platforms like OpenShift.
- Strong programming skills in Python; familiarity with Go, Ruby, or Bash scripting.
- Experience with common ML libraries such as scikit-learn, TensorFlow, Keras, and PyTorch.
- Experience with software engineering guidelines, including version control, testing, and automation.
- Ability to understand data science workflows, experiment tracking, and feature engineering tools.
- Strong communication skills; ability to work collaboratively in multi-functional teams.
- Knowledge of cloud services such as AWS Sagemaker, Azure ML, and GCP Vertex AI.
- Exposure to feature stores like Feast, Tecton, or Databricks Feature Store.
- Experience with observability tools (Prometheus, Grafana, ELK, CloudWatch, Datadog).
- Experience implementing model governance and lineage with tools like MLflow Registry, SageMaker Model Registry, or Vertex ML Metadata.
- Familiarity with infrastructure-as-code (Terraform, CloudFormation).