Lead Applied AI Software Engineer ( AI)

About the role

The Lead Applied AI Engineer plays a pivotal role in driving AI excellence within Humana’s Insurance and CenterWell business segments. This role is instrumental in architecting and delivering advanced AI systems that seamlessly integrate Generative AI capabilities and agents, integrating into secure, scalable healthcare platforms handling millions of member interactions.

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.

• 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.

• Drive 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.

• Establish organization-wide best practices for prompt engineering. These practices include systematic testing and version control, comprehensive evaluation frameworks that combine automated metrics with human assessment, model observability including tracking of costs and performance, and performance benchmarking methodologies. The latter enable data-driven optimization decisions.

• Ensure AI solutions rigorously meet healthcare compliance requirements through comprehensive documentation of system behavior and decision logic. Practical experience addressing deployment challenges in regulated environments is necessary, including testing, documentation, change management, and ongoing monitoring requirements.

Qualifications

• Use your skills to make an impact. Over 7 years of experience in software engineering with a strong focus on applied AI/ML. This experience includes building and operating distributed systems at scale, as well as developing full-stack architectures that combine backend services with modern web applications. Leadership of significant projects has delivered measurable business impact through AI capabilities.

• Required Qualifications: Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience through significant technical leadership in AI projects, making recognized contributions to the AI engineering community, or achieving progressive career advancement into increasingly responsible AI technical leadership roles.

• Demonstrated deep expertise designing and deploying production-grade generative AI systems. These systems included sophisticated RAG architectures with multi-hop retrieval and reasoning, as well as agent orchestration frameworks that coordinate multiple AI agents with tool use and memory. Additionally, they featured multi-model systems that combine different AI capabilities, and conversational AI systems that maintain context and handle complex dialogues.

• Complex AI initiatives across multiple teams with different specializations. This involves translating high-level business objectives into concrete AI system designs and technical roadmaps. Additionally, I coordinate implementation across frontend, backend, data, and infrastructure teams. Finally, I drive projects from conception through production deployment and ongoing optimization.

• Strong technical proficiency in Python, including advanced language features and design patterns. Extensive experience with modern web application frameworks, such as React and FastAPI, and familiar with best practices for scalability and maintainability. Deep knowledge of AI-specific technologies, including vector databases, embedding models, LLM APIs, and orchestration frameworks.

• Demonstrated experience establishing organization-wide best practices for prompt engineering. These practices include systematic testing and version control, comprehensive evaluation frameworks that combine automated metrics with human assessment, model observability including tracking of costs and performance, and performance benchmarking methodologies. The latter enable data-driven optimization decisions.

• Deep familiarity with responsible AI principles is essential, including fairness, accountability, transparency, and ethics. Understanding of governance considerations for AI systems is also crucial, including model risk management and validation requirements. Practical experience addressing deployment challenges in regulated environments is necessary, including testing, documentation, change management, and ongoing monitoring requirements.