Job Description

• Strong proficiency in end-to-end machine learning engineering, including data preparation, feature pipelines, deployment, and monitoring.
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or similar.
• Backend or full-stack development experience with one or more languages (Python, Java, JavaScript/Node, Go, etc.).
• Familiarity with cloud environments (AWS, Google Cloud Platform, Azure) and containerization (Docker, Kubernetes).
• Experience building automated CI/CD pipelines for ML workflows.
• Strong understanding of model versioning, reproducibility, and experiment tracking.
• Ability to work in a fast-paced environment and collaborate across data science, engineering, and product teams.

• Experience building internal ML platforms or developer tools.
• Knowledge of distributed systems and large-scale data processing (Spark, Flink, Beam, etc.).
• Familiarity with monitoring tools for ML models (e.g., Evidently AI, Fiddler, Arize, WhyLabs).
• Experience deploying multiple models in production environments.

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