The Opportunity
As an ML Engineer, you will be responsible for building and maintaining the pipelines that power AI within Healthcare Information Systems (HIS). This role suits a practical, detail focused engineer with a strong interest in MLOps, data reliability, and production stability.
You will focus on ensuring models work reliably in real world environments. The position bridges data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring AI services are secure and scalable.
Key Responsibilities
1. MLOps & Deployment
- Build and maintain CI/CD pipelines for machine learning, including automated testing, model deployment, and version control (for example MLflow or Git).
- Deploy ML models as scalable APIs and microservices while meeting performance and latency requirements for clinical use.
- Implement monitoring tools to track model performance, data drift, and overall system health in production.
2. Data Engineering & Integration
- Develop and optimise ETL pipelines to transform healthcare data (FHIR, HL7) into clean, usable datasets for training and inference.
- Support feature store and data layer development to ensure consistency between training and production environments.
- Collaborate with backend engineering teams to integrate ML outputs into core healthcare applications.
3. Engineering Best Practices
- Write clean, maintainable, well documented Python code and participate in code reviews.
- Use Docker and Kubernetes to containerise and orchestrate ML workloads across environments.
- Follow security and compliance standards to ensure data handling and deployments meet HIPAA and HITRUST requirements.
Candidate Profile
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.
- 3 to 5 years of professional experience in software or data engineering, including at least 2 years in machine learning production environments.
Core Technical Stack
- Strong Python skills with working knowledge of SQL.
- Experience with at least one major cloud platform (AWS, Azure, or GCP).
- Hands on experience with containerisation tools such as Docker.
- Familiarity with ML libraries such as PyTorch or Scikit-learn.
- Exposure to MLOps tooling (for example Airflow, Prefect, BentoML, or Kubeflow).
- Experience with data processing tools such as Pandas, Spark, or dbt.
- Knowledge of a compiled language like Go or Java is a plus.
Preferred Qualifications
- Experience deploying Large Language Models (LLMs) or using frameworks such as LangChain.
- Experience working in regulated industries such as healthcare or finance.
- Understanding of API design and microservices architecture.