Lead ML/AI Engineer

This is a fully remote position, open to applicants in Tennessee.

📋 Description

• Take ownership of the decision-making and feedback layer that connects scoring to actions, which includes action selection and ranking, experiment and holdout assignments, outcome tracking, production ROI calculations, and lift-measurement pipelines.

• Manage the engineering implementation, ensuring auditability and reproducibility of the production savings calculations.

• Create event-driven, real-time integrations between model outputs and action systems, encompassing streaming pipelines, versioned schemas, replay mechanisms, ordering, idempotency, and failure management.

• Establish cost-to-serve standards and budgets for compute, streaming, inference, and LLM expenditures on a per-model and per-decision basis.

• Define the production benchmarks for ML models, which include CI/CD, model registries, alerting systems, drift monitoring, coverage assessments, and calibrations across cohorts, markets, and payers.

• Oversee the target architecture and technical roadmap utilizing AWS-native services and the Databricks lakehouse, focusing on infrastructure as code, IAM, secure data management, containerized inference, and managed foundation-model services under PHI controls.

• Establish technical direction and production standards for GenAI applications, LLM pipelines, and agentic workflows, including evaluation criteria, retrieval processes, guardrails, and cost management.

• Collaborate with data science, medical economics, data engineering, software engineering, clinical, and operations teams.

• Uphold engineering standards through code reviews, automated testing, reproducibility measures, documentation, and disciplined release processes.

• Lead design reviews, document architectural decisions, mentor engineers and data scientists, and advocate for technical positions with leadership, finance, clinical, and operations stakeholders.

• Perform additional duties as assigned.

⛳️ Requirements

• Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent experience.

• 8+ years of experience in ML engineering, MLOps, or software engineering.

• 4+ years of experience operating ML systems in a production environment.

• 2+ years serving as the technical lead or architect for a production ML or decision-making system.

• Experience in building or managing prescriptive production systems such as recommendation engines, decisioning platforms, prioritization systems, or experimentation frameworks.

• Proven production experience with AWS data and ML services and infrastructure as code, including Terraform, CDK, or CloudFormation.

• Experience with Databricks, PySpark, Delta Lake, MLflow, or similar technologies in a production setting.

• Practical experience in designing and operating event-driven or streaming systems like Kinesis or Kafka, including schema or contract management.

• Proficient in Python and SQL.

• Hands-on experience running ML workloads in production, including training pipelines, model serving, feature systems, or ML platform tools.

• Knowledge of CI/CD processes for ML workloads and model and pipeline monitoring, including calibration across diverse populations.

• A proven track record of establishing engineering standards through review and mentorship.

• Willingness to travel up to 10% for onsite meetings, team collaboration, and company events.

• Preferred: Experience with scikit-learn, XGBoost, or PyTorch.

• Preferred: Familiarity with healthcare economics or value-based care.

• Preferred: Experience with dbt and lakehouse data modeling.

• Preferred: Understanding of healthcare data and PHI handling requirements.

• Preferred: Experience with uplift modeling, bandit or sequential decisioning, or holdout measurement methodologies.

• Preferred: Knowledge of containerized model serving and staged or canary deployment approaches.

• Preferred: Familiarity with Amazon Bedrock or other managed foundation-model platforms under a HIPAA BAA.

• Preferred: Experience in production with agentic frameworks and retrieval systems.

• Preferred: Experience with LLM-based applications, including evaluation processes and guardrails.

🏝️ Benefits

• Remote-first work environment with options for a hybrid work model in Nashville.

• Generous paid time off (PTO) and holiday policies.

• Paid parental leave.

• Comprehensive medical, dental, vision, life, and disability insurance effective from Day 1.

• Access to wellness resources.

• Employer HSA contributions.

• 401(k) plan with employer matching.

• Flexibility and autonomy to support work-life balance.

• Meaningful work within a mission-driven environment.

• An inclusive workplace culture.