Lead Applied AI Software Engineer ( AI)

Become a part of our caring community The Enterprise AI organization at Humana is a pioneering force, driving AI development across our Insurance and CenterWell 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. This engineer will architect and deliver advanced AI systems. These systems will seamlessly integrate Generative AI capabilities and agents. They will integrate into secure, scalable healthcare platforms. These platforms handle millions of member interactions. They maintain the highest standards of data privacy and system reliability.

This highly technical and influential role defines technical standards for AI deployment across the organization. It ensures that AI systems are reliable through rigorous testing and monitoring, measurable through comprehensive metrics and evaluation frameworks. Additionally, it ensures compliance with healthcare regulations and ethical guidelines, and strategic alignment 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.

Key Responsibilities

• Architect comprehensive end-to-end AI systems, including sophisticated RAG pipelines with multi-stage retrieval and re-ranking. These pipelines are designed with appropriate modularity, extensibility, and operational characteristics to support evolving business requirements. Additionally, the systems include complex agent orchestration systems that coordinate multiple specialized agents. Furthermore, they feature multi-model integrations that leverage different AI models for their respective strengths.
• Define rigorous standards for prompt engineering, including templates, versioning, and testing methodologies. Establish comprehensive evaluation metrics that capture both technical performance and business value. 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. This includes detailed logging and tracing. Comprehensive reliability is also crucial, featuring graceful degradation and circuit breakers. Monitoring is essential, with real-time dashboards and automated alerting. Additionally, robust incident response procedures are necessary. The goal is to ensure 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. 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. Implement A/B testing capabilities, analyze user feedback and system telemetry, and systematically iterate on prompts, retrieval strategies, and model configurations. This progressive iteration improves system performance and user satisfaction over time.
• Collaborate strategically with platform teams to ensure infrastructure readiness for demanding …