Lead Machine Learning Engineer – Next Best Action (NBA) Platform

Humana Inc. is committed to putting health first, and they are seeking a Lead Machine Learning Engineer to architect and manage machine learning systems for real-time personalized decision-making. The role involves leading the creation of ML pipelines and decision-time scoring logic to enhance operational effectiveness and model quality.

Responsibilities:

• Design and manage end-to-end machine learning systems, including:
• Feature engineering and reuse strategies
• Offline training pipelines
• Online inference and scoring services
• Model versioning, rollout, and rollback procedures
• Ensure systems meet stringent requirements for latency, scalability, reliability, and correctness in real-time contexts
• Define clear separation between model development, deployment, and downstream decision logic
• Build and operationalize models such as:
• Propensity or likelihood prediction
• Uplift or incremental impact models
• Engagement or responsiveness scoring
• Design models to be composable, explainable, and robust for automated decision workflows
• Collaborate with analytics and product teams to translate business objectives into measurable modeling outcomes
• Apply AI-assisted and agentic approaches to boost ML engineering productivity, including:
• Automated code generation and refactoring for pipelines and services
• Feature exploration and validation
• Intelligent experiment tracking and comparison
• Enhanced debugging and root-cause analysis
• Assess and adopt modern tools to accelerate experimentation, reduce manual overhead, and ensure reliable model operations
• Focus on implementing practical, production-ready AI tools
• Develop and sustain robust MLOps practices, including:
• Continuous training and deployment pipelines
• Online model monitoring for latency, drift, and stability
• Safe rollout strategies (e.g., canary, shadow, phased releases)
• Fallback mechanisms for model degradation or unavailability
• Guarantee model outputs are traceable, reproducible, and auditable
• Serve as the technical leader for ML engineering, establishing standards and best practices
• Partner with software engineers, data engineers, and platform teams to ensure seamless integration of ML systems into production
• Mentor machine learning engineers and contribute to the overall maturity of engineering teams
• Influence architectural decisions to ensure testability, observability, and resilience

Requirements:

• 8+ years of experience in machine learning engineering, applied ML, or data-driven platform development
• 3+ years in a technical lead or senior ML engineering capacity
• Deep expertise in feature engineering and data pipelines
• Deep expertise in model training and evaluation
• Deep expertise in real-time or near-real-time inference systems
• Strong software engineering skills in Python, Java, or similar languages
• Practical experience with AI-assisted development tools to streamline ML workflows
• Experience with personalization, recommendation, or decisioning platforms
• Familiarity with distributed systems and event-driven architectures
• Experience deploying models in regulated or high-reliability settings
• Knowledge of model explainability and fairness methodologies