Full-Time Senior Machine Learning Platform/Ops Engineer

• Own the ML lifecycle: Design, implement, and maintain robust, containerized, and reproducible pipelines for model training, evaluation, and deployment—across both batch and real-time settings.
• Operationalize models at scale: Build and manage ML services, APIs, and model serving infrastructure using tools like MLflow, Amazon SageMaker, and Feature Store.
• Automate and monitor: Set up and maintain monitoring, observability, and alerting systems to ensure high availability and performance (including model/data drift, feature logging, and inference latency).
• Accelerate experimentation: Develop and maintain internal libraries, templates, and platform tooling to improve reproducibility and simplify deployment workflows for all model teams.
• Ensure reliability and quality: Implement CI/CD pipelines for model and data workflows using Docker, Terraform, and Jenkins and share best practices, mentor less experienced engineers, and foster strong collaboration across teams.
• Stay current: Continuously evaluate emerging MLOps technologies to improve efficiency, scalability, and reliability.

Hands-on MLOps experience: 2+ years production experience operationalizing, deploying, monitoring, and maintaining ML models at scale.

Tooling: Proficient with infrastructure-as-code, CI/CD systems (Docker, Terraform, Jenkins, or equivalent), and at least one major cloud provider (AWS, GCP, or Azure).

Programming: Strong Python skills (including ML libraries such as scikit-learn, LightGBM, PyTorch, TensorFlow; plus experience with SQL).

Monitoring: Familiar with monitoring and logging for ML pipelines (model drift detection, data validation, performance/feature logging).

Collaboration: Effective communicator with experience partnering across engineering and data science.

Bonus: Experience with feature stores, model version management, or building internal ML platforms.

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