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Job Description
- Architect and optimize ML infrastructure with Kubeflow, MLflow, SageMaker Pipelines.
- Build CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI/CD).
- Automate ML workflows including feature engineering, retraining, and deployment.
- Scale ML models using Docker, Kubernetes, and Airflow.
- Ensure model observability, security, and cost optimization on cloud platforms (AWS/GCP/Azure).
- Must-Have Skills:
- Proficiency in Python, TensorFlow, PyTorch, and CI/CD pipelines.
- Hands-on experience with cloud ML platforms like AWS SageMaker, GCP Vertex AI, or Azure ML.
- Expertise in monitoring tools such as MLflow, Prometheus, and Grafana.
- Knowledge of distributed data processing frameworks like Spark and Kafka.
- (Bonus) Experience in A/B testing, canary deployments, or serverless ML.