Senior ML Engineer

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