Senior Machine Learning Engineer - Machine Learning Operations

About the Role

Hello Heart is seeking a Senior Machine Learning Engineer with strong MLOps abilities to own the production ML systems behind user engagement, cardiovascular risk stratification, and personalized health recommendations in the Hello Heart app. You'll build and run the CI/CD, real-time serving, versioning, and monitoring infrastructure that models depend on, working closely with data scientists who own the modeling to make sure what they build runs reliably at scale. You should have a proven track record of shipping ML systems to production and be comfortable using AI coding assistants as a core part of your workflow.

Responsibilities

• Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.
• Serve models in real time at scale, with a strong focus on low-latency inference, reliability, and graceful degradation under load, and operate batch inference with clear standards for latency, availability, correctness, and cost.
• Write high-quality, maintainable, well-tested production-grade code, and own its observability, debugging, reliability, and scalability in production.
• Implement model monitoring for drift, data quality, latency, and performance degradation, with automated retraining.

Requirements

• 4+ years of software engineering experience, including 2+ years of hands-on MLOps, building and owning production ML infrastructure on AWS or GCP with a data or ML platform (e.g., Snowflake ML, Databricks, SageMaker, Vertex AI, JFrog ML).
• Strong Python skills, with production-grade, maintainable, well-tested code.
• Hands-on experience building CI/CD pipelines for ML: automated testing, training, validation, and deployment of models (e.g., Cloud Build, Cloud Deploy, Jenkins, GitHub Actions).
• Experience serving models in real time at scale: low-latency inference, autoscaling, and reliable serving architectures.
• Experience owning a solution end-to-end, from pipeline and model integration through deployment and production debugging.
• Proficiency using AI coding assistants as a core part of the development workflow.

Nice-to-Haves

• Hands-on model development experience: feature engineering, training, and evaluation, with frameworks such as PyTorch, scikit-learn, XGBoost, or LightGBM (plus YOLO/OCR, supervised/unsupervised learning).
• Hands-on experience integrating LLMs into production systems (e.g., LangChain, LangGraph, AWS Bedrock, RAG).
• Familiarity with ML lifecycle tooling: experiment tracking, model registries, feature stores (MLflow, DVC, Feast, Weights & Biases).
• Experience with workflow orchestration tools (Airflow, Dagster, Prefect, Kubeflow Pipelines).
• Experience with monitoring/observability stacks (Prometheus, Grafana, Datadog).