🛠 Tech Stack

💼 About This Role

You'll own the infrastructure and operational lifecycle of ML systems powering a clinical monitoring platform. You'll build and maintain production ML pipelines, deployment infrastructure, and monitoring systems. This high-ownership role directly impacts patient outcomes by ensuring predictive models are always running and accurate.

🎯 What You'll Do

• Own and extend ML pipeline orchestration using Apache Airflow.
• Deploy and manage ML models on AWS infrastructure (Batch, S3).
• Maintain MLflow experiment tracking and model registry infrastructure.
• Build monitoring systems for model performance, data drift, and alerting.

📋 Requirements

• 4+ years experience in MLOps or related infrastructure role.
• Strong proficiency in Python for ML pipeline development.
• Hands-on experience with Apache Airflow for pipeline orchestration.
• Experience deploying ML workloads on AWS (Batch, EC2, S3).

✨ Nice to Have

• Experience deploying models to edge or embedded devices.
• Background in healthcare or medical devices.
• Familiarity with model serving frameworks (TorchServe, TF Serving).

🎁 Benefits & Perks

• 🏖️ Unlimited PTO
• 🏥 Health insurance
• 💰 Equity package
• 📈 Professional development budget
• 🏠 Remote-friendly culture