Live opening · Posted 27 days ago
The key details from the original listing.
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Description supplied by the original job listing.
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).
Experience
11-15 yrs
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