Job Description

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‍Responsibilities‍

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• Design and implement MLOps pipelines for training, validation, deployment, and monitoring of machine learning models.
• Develop and maintain infrastructure for data versioning, model registries, and experiment tracking (e.g., MLflow, LakeFS, Airflow).
• Integrate orchestration tools (e.g., Kubeflow, Ray, Airflow) to support automated workflows and distributed training.
• Collaborate with data scientists and software engineers to ensure seamless model handoff and deployment.
• Build APIs and SDKs to abstract infrastructure complexity and enable self-service model development.
• Implement monitoring and alerting systems for model drift, performance degradation, and system health.
• Support on-prem and cloud-based deployments (e.g., Kubernetes, HPC clusters, AWS).

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‍Qualifications‍

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field.
• 3+ years of experience in software development, preferably in AI/ML infrastructure or data platforms.
• Proficiency in Python and/or TypeScript/JavaScript.
• Experience with backend frameworks (e.g., FastAPI, Flask, Node.js) and frontend libraries (e.g., React, Vue).
• Familiarity with cloud services (AWS preferred), containerization (Docker), and orchestration (Kubernetes).
• Strong understanding of RESTful APIs, CI/CD pipelines, and Git-based workflows.

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Preferred Qualifications

• Experience with distributed training frameworks (e.g., Ray, Ray Tune)
• Knowledge of model explainability, monitoring, and rollback strategies.
• Exposure to hybrid cloud/on-prem infrastructure and HPC environments.
• Prior work on internal platforms or developer tools.

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