Machine Learning Engineer- AI Data Platform; Reno, NV

Machine Learning Engineer- AI Data Platform (Reno, NV)

We are seeking a highly skilled AI Engineer to serve as a core builder of our AI Data Platform. This role sits at the intersection of machine learning engineering, data platform development, and business intelligence, with responsibility for designing and operating the infrastructure that powers AI‑driven insights across the organization.

You will build intelligent data pipelines, production‑grade ML systems, and AI‑enabled features that translate complex data into actionable outcomes. This role is ideal for an engineer who enjoys working end‑to‑end from data ingestion and feature engineering to model deployment and downstream consumption in analytics and BI tools.

Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

• Build AI‑first data pipelines: Design, implement, and maintain scalable data pipelines that support model training, inference, and analytics use cases across the AI Data Platform.
• Deploy production ML systems: Develop, deploy, and monitor machine learning models using AWS Sage Maker, ensuring reliability, observability, and performance in production environments.
• Implement Retrieval‑Augmented Generation (RAG): Architect and maintain RAG‑based systems that combine structured and unstructured data to power AI‑driven insights and applications.
• Operationalize ML lifecycle management: Use MLflow for experiment tracking, model versioning, and lifecycle management to support reproducibility and continuous improvement.
• Design feature infrastructure: Build and manage feature stores (e.g., Feast, Tecton, or Sage Maker Feature Store) to ensure consistent, reusable features across training and inference.
• Orchestrate complex workflows: Create and manage Apache Airflow DAGs to orchestrate data transformations, model pipelines, and AI workflows with clear dependencies and monitoring.
• Enable analytics consumption: Partner with BI and analytics teams to ensure ML outputs integrate cleanly with our internal BI reporting hub.
• Translate business questions into AI solutions: Collaborate with stakeholders to convert ambiguous business problems into measurable ML‑ and data‑driven solutions.
• Uphold data quality and governance: Ensure AI pipelines and models adhere to data governance, security, and quality standards, particularly when handling sensitive data.
• Collaborate cross‑functionally: Work closely with Data Science, Analytics Engineering, Medical Economics, and Data Ops to align AI platform capabilities with business priorities.

Required:

• Five to Seven Years in the ML Engineering space.
• Strong proficiency in Python and SQL for data processing, modeling, and pipeline development.
• Hands‑on experience building and deploying machine learning models in production, including monitoring and performance management.
• Experience with AWS‑based ML infrastructure, including Sage Maker for training, deployment, and inference.
• Practical experience designing or operating Retrieval‑Augmented Generation systems that integrate LLMs with enterprise data sources.
• Experience using MLflow (or equivalent) for experiment tracking, model registry, and lifecycle management.
• Experience with Apache Airflow for orchestration of data and ML pipelines.
• Strong foundation in data engineering concepts, including data modeling, versioning, and testing.
• Ability to partner with Med Econ and BI teams to ensure ML outputs are interpretable, trusted, and consumable.

Preferred:

• Experience with AWS Bedrock and/or Aider for LLM orchestration or AI‑assisted development workflows.
• Experience with dbt for transformation modeling, testing, and documentation.
• Familiarity with feature store architectures (Feast, Tecton, Sage Maker Feature Store).
• Experience integrating ML outputs into BI tools such as Tableau, Looker, or Quick Sight.
• Experience with CI/CD pipelines, Git‑based workflows, and infrastructure‑as‑code practices.
• Exposure to healthcare or regulated data environments is a plus but not required.

Nice to Have:

• Working knowledge of Docker and Kubernetes for scalable deployment of ML…