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Selected role

Senior ML Engineer

HCA Healthcare

Senior Full-time

Job Summary and Qualifications

Drives implementation of AI Platform capabilities within an embedded product team through hands-on development and technical expertise. Actively contributes to AI development while following platform standards and patterns, writing high-quality code, and providing practical feedback for platform improvement. Leverages platform solutions to solve team-specific MLOps challenges while balancing team-specific needs with organizational standards. Partners with both product and platform teams to ensure effective adoption of platform capabilities, accelerating AI delivery through consistent implementation patterns and engineering excellence

What you will do in this role:

Actively contribute to AI/MLOps development within assigned product team
Implement platform capabilities and patterns effectively
Write high-quality, maintainable code following team standards
Develop and maintain ML systems using platform capabilities
Implement robust CI/CD pipelines for ML models
Ensure proper testing and validation of ML systems
Balance team-specific needs with platform standardization
Champion platform adoption within a team
Follow and help refine platform patterns and best practices
Identify opportunities for leveraging platform capabilities
Provide feedback on platform features and usability
Help validate platform patterns through direct implementation
Share knowledge and experiences with other MLEs
Contribute to platform documentation and examples
Participate in code reviews with focus on platform patterns
Implement and maintain ML pipelines using platform tools
Document technical decisions and implementations
Follow established best practices and standards
Contribute to technical discussions and design reviews
Work closely with product team to understand AI development needs
Provide implementation feedback to platform team
Participate in technical discussions and knowledge sharing
Help identify opportunities for platform improvement

What qualifications you will need:

Bachelor's degree - Required
Master's degree - Preferred
5+ years of experience in software engineering with a focus on ML and AI System Engineering - Required
Experience working in as an embedded engineer in a cross-function product team - Preferred
Or equivalent combination of education and/or experience

Additional Requirements/Preferences

Strong technical background in ML engineering with demonstrated coding expertise - Required
Strong Python development expertise with focus on ML systems and AI/MLOps - Required
Familiarity of ML workflows and operational requirements - Required
Hands-on experience implementing model CI/CD pipelines - Required
Experience with modern Python development practices including type checking, testing frameworks, and package management - Required
History of successful collaboration with product teams - Required
Familiarity with ML Development Lifecycle management and MLOps best practices - Required
Familiarity of ML Monitoring and observability - Required
Experience with LLMs and Infrastructure - Preferred
Familiarity integrating with feature stores, feature caches and model serving platforms - Preferred
Understanding of ML/AI platform tooling and patterns - Preferred
Familiarity with Distributed model training - Preferred
Hands-on experience with Kubeflow, Argo, MLFlow or other ML/AI Training orchestrators - Preferred
Familiarity of ML/AI metadata tools and model registries – Preferred
Hands-on experience with Terraform or other IaC tools - Preferred
Hands on experience building ML/AI solutions on GCP and Vertex AI - Preferred
Familiarity with ML model lifecycle and common ML libraries (PyTorch, scikit-learn, XGBoost, AutoGluon, CatBoost, TensorFlow, Keras) - Preferred
Familiarity with major LLM Models (Gemini, Cluade, ChatGPT, DeepSeek, LLaMA) - Preferred
Experience with FastAPI and async Python development - Preferred
Familiarity with modern Python tooling (uv, mypy, ruff, bandit) - Preferred
Experience with prompt management and versioning systems - Preferred
Understanding of RAG architectures and token optimization - Preferred
Experience writing and optimizing ML/AI tooling and components in C++ or Rust - Preferred
Experience with LLMs and Infrastructure - Preferred
Experience integrating with feature stores, feature caches and model serving platforms – Preferred

Work Location/Schedule:

Remote - M-F, 8am – 5pm - Central Time

Travel Required:

This job requires travel to Nashville, TN to attend final interview, 3-day New Hire Orientation, quarterly team meetings, and other travel on as-needed basis

Visa Sponsorship:

Not Offered, now or in the future

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