AI Platform Engineer

As an MLOps Engineer, you will be responsible for the following key responsibilities: - Designing and implementing MLOps pipelines for training, validation, deployment, and monitoring of machine learning models. - Developing and maintaining infrastructure for data versioning, model registries, and experiment tracking using tools like MLflow, LakeFS, and Airflow. - Integrating orchestration tools such as Kubeflow, Ray, and Airflow to support automated workflows and distributed training. - Collaborating with data scientists and software engineers to ensure seamless model handoff and deployment. - Building APIs and SDKs to abstract infrastructure complexity and enable self-service model development. - Implementing monitoring and alerting systems for model drift, performance degradation, and system health. - Supporting on-prem and cloud-based deployments using technologies like Kubernetes, HPC clusters, and AWS. Qualifications required for this role include: - Bachelor's or Master's degree in Computer Science, Software Engineering, or a 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 such as FastAPI, Flask, and frontend libraries like React and Vue. - Familiarity with cloud services (preferably AWS), containerization (Docker), and orchestration (Kubernetes). - Strong understanding of RESTful APIs, CI/CD pipelines, and Git-based workflows. Preferred qualifications for the role may include: - Experience with distributed training frameworks like Ray and 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. As an MLOps Engineer, you will be responsible for the following key responsibilities: - Designing and implementing MLOps pipelines for training, validation, deployment, and monitoring of machine learning models. - Developing and maintaining infrastructure for data versioning, model registries, and experiment tracking using tools like MLflow, LakeFS, and Airflow. - Integrating orchestration tools such as Kubeflow, Ray, and Airflow to support automated workflows and distributed training. - Collaborating with data scientists and software engineers to ensure seamless model handoff and deployment. - Building APIs and SDKs to abstract infrastructure complexity and enable self-service model development. - Implementing monitoring and alerting systems for model drift, performance degradation, and system health. - Supporting on-prem and cloud-based deployments using technologies like Kubernetes, HPC clusters, and AWS. Qualifications required for this role include: - Bachelor's or Master's degree in Computer Science, Software Engineering, or a 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 such as FastAPI, Flask, and frontend libraries like React and Vue. - Familiarity with cloud services (preferably AWS), containerization (Docker), and orchestration (Kubernetes). - Strong understanding of RESTful APIs, CI/CD pipelines, and Git-based workflows. Preferred qualifications for the role may include: - Experience with distributed training frameworks like Ray and 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.