Lead Applied AI Engineer

The Enterprise AI organization at Humana is a pioneering force, driving AI innovation across our Insurance and Center Well business segments. By collaborating with world-leading experts, we are at the forefront of delivering cutting-edge AI technologies for improving care quality and experience of millions of consumers. Our goal is to create safe AI solutions that will revolutionize and improve healthcare experience and outcomes for our customers.

We are actively seeking top talent to join us in shaping the future of healthcare through AI excellence. Join our rapidly expanding team of dedicated product managers, data scientists, engineers, policy experts, and business leaders as we work together to build impactful and beneficial AI systems.

At Humana, applied artificial intelligence is central to driving intelligent automation that reduces administrative burden, enabling personalization that delivers tailored member experiences, and optimizing operational efficiency across the complex healthcare ecosystem. We are seeking an accomplished Lead Applied AI Engineer to architect and deliver advanced AI systems that seamlessly integrate Generative AI capabilities and intelligent agents into secure, scalable healthcare platforms that handle millions of member interactions while maintaining the highest standards of data privacy and system reliability.

This highly technical and influential role defines technical standards for AI deployment across the organization, ensures that AI systems are reliable through rigorous testing and monitoring, measurable through comprehensive metrics and evaluation frameworks, compliant with healthcare regulations and ethical guidelines, and strategically aligned with enterprise architecture and business strategy. The Lead Applied AI Engineer will operate at the critical intersection of AI innovation and responsible healthcare technology, balancing the rapid pace of AI advancement with the careful, deliberate approach required in healthcare environments.

Architect comprehensive end-to-end AI systems including sophisticated RAG pipelines with multi-stage retrieval and re-ranking, complex agent orchestration systems that coordinate multiple specialized agents, and multi-model integrations that leverage different AI models for their respective strengths, designing these systems with appropriate modularity, extensibility, and operational characteristics to support evolving business requirements.

Define rigorous standards for prompt engineering including templates, versioning, and testing methodologies, establish comprehensive evaluation metrics that capture both technical performance and business value, and develop performance optimization strategies including model selection criteria, caching approaches, and resource utilization patterns that teams across the organization can adopt to accelerate AI delivery.

Lead deployment of AI systems into production environments with strong observability including detailed logging and tracing, comprehensive reliability including graceful degradation and circuit breakers, thorough monitoring including real-time dashboards and automated alerting, and robust incident response procedures, ensuring AI services meet stringent service level objectives required for healthcare applications.

Design scalable data ingestion architectures that can process diverse data sources including structured databases, unstructured documents, and real-time streams, implement efficient retrieval architectures using vector databases and hybrid search approaches, develop data preprocessing pipelines that clean and enrich data for AI consumption, and establish data quality monitoring to ensure AI systems operate on high-quality inputs.

Drive quantitative evaluation and continuous improvement of AI systems through establishment of evaluation frameworks, implementation of A/B testing capabilities, analysis of user feedback and system telemetry, and systematic iteration on prompts, retrieval strategies, and model configurations to progressively improve system performance and user satisfaction over time.

Collaborate strategically with platform teams to ensure infrastructure readiness for demanding AI workloads including GPU availability, appropriate networking configurations, and optimized data storage, define requirements for AI-specific platform capabilities such as model serving infrastructure and feature stores, and partner on integration of AI systems with enterprise services.

Mentor engineers at various levels through technical guidance, code reviews, architecture discussions, and career development support, elevate AI engineering best practices across the organization through creation of documentation, delivery of training sessions, and establishment of communities of practice, and foster a culture of responsible AI development that prioritizes ethics, transparency, and user benefit.

Ensure AI…