What are the responsibilities and job description for the ML Engineer position at Haystack?
A global healthcare technology company, dedicated to improving patient care and transforming healthcare delivery through innovative information systems and solutions, is seeking a skilled ML Engineer.
The Impact You’ll Make in this Role
As an ML Engineer, you will be responsible for building and maintaining the pipelines that power AI in our Healthcare Information Systems (HIS). This role focuses on ensuring models work reliably in the real world, bridging the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring AI services are secure and scalable.
The Role
The Impact You’ll Make in this Role
As an ML Engineer, you will be responsible for building and maintaining the pipelines that power AI in our Healthcare Information Systems (HIS). This role focuses on ensuring models work reliably in the real world, bridging the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring AI services are secure and scalable.
The Role
- Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git).
- Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use.
- Implement basic monitoring tools to track model performance, data drift, and system health in production.
- Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference.
- Help build and maintain feature stores and data layers that ensure consistency between training and production environments.
- Work closely with backend teams to integrate ML outputs into core healthcare applications.
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
- 3–5 years of professional experience in software engineering or data engineering, with at least 2 years focused on machine learning production environments.
- Strong proficiency in Python and familiarity with SQL; knowledge of a compiled language (like Go or Java) is a plus.
- Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker).
- Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow).
- Experience with data processing frameworks (like Pandas, Spark, or dbt).
- Opportunity to build and maintain AI-powered healthcare information systems.
- Work on cutting-edge MLOps, data reliability, and production stability challenges.
- Collaborate with data science and software engineering teams.