Machine Learning Engineer
HaystackAbout the role
A pioneering technology company is revolutionising healthcare through advanced AI solutions. They are dedicated to integrating cutting-edge machine learning into critical healthcare information systems to enhance patient care and operational efficiency.
The Role
Build and maintain robust pipelines for AI in Healthcare Information Systems (HIS).
Bridge the gap between data science and software engineering by implementing automated MLOps workflows.
Deploy ML models as scalable APIs and microservices, ensuring performance and latency for clinical use.
Develop and optimize ETL processes for transforming healthcare data (FHIR, HL7) into usable datasets.
Implement monitoring tools to track model performance, data drift, and system health in production.
Ensure data handling and deployments meet HIPAA and HITRUST security standards.
What You'll Need
3-5 years of professional experience in software or data engineering, with at least 2 years in ML production.
Strong proficiency in Python; familiarity with SQL and compiled languages (Go/Java) is a plus.
Hands-on experience with a major cloud provider (AWS, Azure, or GCP) and Docker/Kubernetes.
Familiarity with ML libraries (PyTorch, Scikit-learn) and MLOps tools (Airflow, Prefect, BentoML, Kubeflow).
Experience with data processing frameworks like Pandas, Spark, or dbt.
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or related field.
What's On Offer
Opportunity to make a significant impact on healthcare through AI innovation.
Work on complex MLOps, data reliability, and production stability challenges.
Exposure to cutting-edge technologies like LLMs and frameworks like LangChain.
Collaborative environment focused on engineering best practices and code quality.
Apply via Haystack today!
Responsibilities
- Build and maintain robust pipelines for AI in Healthcare Information Systems (HIS)
- Bridge the gap between data science and software engineering by implementing automated MLOps workflows
- Deploy ML models as scalable APIs and microservices, ensuring performance and latency for clinical use
- Develop and optimize ETL processes for transforming healthcare data (FHIR, HL7) into usable datasets
- Implement monitoring tools to track model performance, data drift, and system health in production
- Ensure data handling and deployments meet HIPAA and HITRUST security standards
Qualifications
- 3-5 years of professional experience in software or data engineering, with at least 2 years in ML production
- Strong proficiency in Python; familiarity with SQL and compiled languages (Go/Java) is a plus
- Hands-on experience with a major cloud provider (AWS, Azure, or GCP) and Docker/Kubernetes
- Familiarity with ML libraries (PyTorch, Scikit-learn) and MLOps tools (Airflow, Prefect, BentoML, Kubeflow)
- Experience with data processing frameworks like Pandas, Spark, or dbt
- Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or related field
Benefits
- Opportunity to make a significant impact on healthcare through AI innovation
- Work on complex MLOps, data reliability, and production stability challenges
- Exposure to cutting-edge technologies like LLMs and frameworks like LangChain
- Collaborative environment focused on engineering best practices and code quality
Skills mentioned
About Haystack
Haystack combines AI & expert vetting to deliver world-class tech candidates who are engaged, aligned, and ready to interview. We're trusted by over 400,000+ tech candidates, working in Software Engineering, Data, Design, DevOps, Cloud, Tech Management, Testing, Product & Delivery, Architecture and more. 100s of employers from startups and scale-ups like Atom Bank, DuckDuckGo and Goodlord to established enterprises like American Express, Dunelm and AWS use Haystack to connect with qualified tech talent that they can't find anywhere else.