Lead Applied AI Software Engineer ( AI)
About the role
The Enterprise AI organization at Humana is seeking a Lead Applied AI Engineer to architect and deliver advanced AI systems. This role will define technical standards for AI deployment across the organization, ensuring reliability through rigorous testing and monitoring, and compliance with healthcare regulations and ethical guidelines.
Responsibilities
Architect comprehensive end-to-end AI systems, including sophisticated RAG pipelines with multi-stage retrieval and re-ranking.
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.
Lead deployment of AI systems into production environments with strong observability.
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.
Collaborate strategically with platform teams to ensure infrastructure readiness for demanding AI workloads.
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.
Foster a culture of responsible AI development that prioritizes ethics, transparency, and user benefit.
Ensure AI solutions rigorously meet healthcare compliance requirements through comprehensive documentation of system behavior and decision logic.
Requirements
Use your skills to make an impact. Over 7 years of experience in software engineering with a strong focus on applied AI/ML, including building and operating distributed systems at scale and developing full-stack architectures that combine backend services with modern web applications.
Demonstrated deep expertise designing and deploying production-grade generative AI systems, including sophisticated RAG architectures with multi-hop retrieval and reasoning, and agent orchestration frameworks that coordinate multiple AI agents with tool use and memory.
Complex AI initiatives across multiple teams with different specializations, including translating high-level business objectives into concrete AI system designs and technical roadmaps, coordinating implementation across frontend, backend, data, and infrastructure teams, and driving projects from conception through production deployment and ongoing optimization.
Strong technical proficiency in Python, including advanced language features and design patterns, and extensive experience with modern web application frameworks like React and FastAPI, as well as deep knowledge of AI-specific technologies such as vector databases, embedding models, LLM APIs, and orchestration frameworks.
Demonstrated experience establishing organization-wide best practices for prompt engineering, including 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.
Deep familiarity with responsible AI principles, including fairness, accountability, transparency, and ethics, and understanding of governance considerations for AI systems, including model risk management and validation requirements.
Practical experience addressing deployment challenges in regulated environments, including testing, documentation, change management, and ongoing monitoring requirements.
Qualifications
Required:
Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
Over 7 years of experience in software engineering with a strong focus on applied AI/ML.
Deep expertise designing and deploying production-grade generative AI systems, including sophisticated RAG architectures with multi-hop retrieval and reasoning, and agent orchestration frameworks that coordinate multiple AI agents with tool use and memory.
Complex AI initiatives across multiple teams with different specializations, including translating high-level business objectives into concrete AI system designs and technical roadmaps, coordinating implementation across frontend, backend, data, and infrastructure teams, and driving projects from conception through production deployment and ongoing optimization.
Strong technical proficiency in Python, including advanced language features and design patterns, and extensive experience with modern web application frameworks like React and FastAPI, as well as deep knowledge of AI-specific technologies such as vector databases, embedding models, LLM APIs, and orchestration frameworks.
Demonstrated experience establishing organization-wide best practices for prompt engineering, including 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.
Deep familiarity with responsible AI principles, including fairness, accountability, transparency, and ethics, and understanding of governance considerations for AI systems, including model risk management and validation requirements.
Practical experience addressing deployment challenges in regulated environments, including testing, documentation, change management, and ongoing monitoring requirements.
PREFERRED:
Experience in healthcare industries.
Proven mentoring and coaching abilities.
Strong cross-functional collaboration skills.