Senior MLOps Engineer (Production)

We are seeking a Senior Machine Learning Engineer to join our team and help shape the future of healthcare technology. In this role, you will design, build, and deploy machine learning systems that power predictive analytics and intelligent decision support in healthcare. You will take ownership of the full ML lifecycle—from data preparation, feature engineering, training, and evaluation to deployment and MLOps practices such as monitoring, scaling, and optimizing production systems. The goal is to ensure that our models are robust, scalable, and cost-efficient.

This position is fully remote, open to candidates based in Europe or India , with periodic team gatherings in Mountain View, California.

What You’ll Do Build reliable, scalable, and reproducible ML pipelines using Python, PySpark, SQL, and Airflow (AWS MWAA).

Deploy ML models into production to address complex healthcare challenges.

Design and manage automated workflows with Docker, ECS / Kubernetes, and CI / CD pipelines.

Collaborate with research teams to transform prototypes into production-ready products.

Implement monitoring, logging, and retraining workflows; participate in the on-call process to support production ML pipelines.

Leverage AWS services (S3, SageMaker, Lambda, Athena, etc.) to train, deploy, and serve models.

Connect models with real-time and batch data sources via APIs and pipelines.

Contribute to code quality, reviews, and system design discussions.

Stay current with new tools, research, and best practices in ML, MLOps / LLMOps, and production-scale AI.

What We’re Looking For 5+ years of applied machine learning and data science experience, with strong MLOps expertise.

Proficiency in Python (Pandas, PySpark, LightGBM, PyTorch, HuggingFace, etc.) and SQL.

Experience with model development, feature engineering, evaluation, and deployment in real-world environments.

Hands-on expertise with Airflow; additional experience with Prefect, Dagster, Kubeflow, or similar tools is a plus.

Strong understanding of AWS infrastructure for ML (S3, SageMaker, ECS, Step Functions).

Experience with Docker and Kubernetes / ECS for packaging and deploying models.

Familiarity with CI / CD for ML (e.g., CircleCI, MLflow, model versioning, testing, automation).

Willingness to participate in the on-call rotation for ML pipeline monitoring and support.

Strong communication skills and ability to thrive in fast-paced, cross-functional teams.

If you’re passionate about building scalable ML systems that make a real-world impact in healthcare, we’d love to hear from you.