ML Engineer

Summary

Turnberry Solutions organizes consultants into practices aligned with its core client services, including Business Transformation. The ML Engineer will build and deploy production machine learning systems, develop healthcare data pipelines, integrate ML outputs into applications, and apply engineering, security, and compliance best practices.

Responsibilities

• MLOps and Deployment
• Pipeline Development: Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git)
• Model Serving: Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use
• Monitoring: Implement basic monitoring tools to track model performance, data drift, and system health in production
• Data Engineering and Integration
• Data Pipelines: Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference
• Feature Management: Help build and maintain feature stores and data layers that ensure consistency between training and production environments
• System Integration: Work closely with backend teams to integrate ML outputs into our core healthcare applications
• Engineering Best Practices
• Code Quality: Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability
• Containerization: Use Docker and Kubernetes to package and orchestrate ML workloads across different environments
• Security and Compliance: Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards

Skills

• • 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
• • Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus
• • Cloud and Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker)
• • ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow)
• • Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt)
• • Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain
• • Experience working in a regulated environment (Healthcare, Finance, etc.)
• • Understanding of API design and microservices architecture

Qualifications

Must Haves

• • 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
• • Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus
• • Cloud and Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker)
• • ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow)
• • Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt)

Nice to Haves

• • Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain
• • Experience working in a regulated environment (Healthcare, Finance, etc.)
• • Understanding of API design and microservices architecture

Benefits

• Comprehensive healthcare package (medical, dental, vision)
• Disability and group term life insurance
• Health and flexible spending accounts
• A utilization bonus
• 401(k) with match
• Flexible time off for salaried employees
• Parental leave for salaried employees
• Flexible work arrangements (all benefits are subject to eligibility requirements)