Lead AI Automation Engineer

Humana•Louisville, KY

10h•Remote

About The Position

The Lead AI Automation Engineer provides hands-on technical leadership for designing, engineering, and productionizing AI-enabled automation across Humana’s Data Governance organization. The role builds reliable automation products and agentic workflows by combining generative AI, workflow orchestration, APIs, enterprise integrations, low-code automation where appropriate, and production engineering practices. It focuses on automation rather than foundational model training or traditional data science. The engineer leads technical design, establishes reusable engineering patterns, mentors engineers, and takes high-value automation opportunities from concept through secure, scalable production deployment. The role also owns production readiness and operational support, including CI/CD, release management, telemetry, incident troubleshooting, continuous improvement, and responsible AI controls.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Engineering, Data Science, Artificial Intelligence, or a related technical field, or an equivalent combination of education and relevant professional experience.
At least 5 years of experience in software engineering, application development, automation engineering, platform engineering, or enterprise systems integration.
At least 2 years of hands-on experience developing AI/ML, generative AI, intelligent automation, or advanced automation solutions, including recent experience building LLM-enabled or agentic solutions.
Experience taking complex automation or AI-enabled solutions from concept or prototype through production deployment and operational support.
Experience providing technical leadership, such as leading solution design, establishing engineering patterns, conducting technical or code reviews, mentoring engineers, or serving as technical lead for complex initiatives.
For home or hybrid home/office work, self-provided internet must support at least 25 Mbps download and 10 Mbps upload; wireless, wired cable, or DSL connection is suggested.
Work from a dedicated space without ongoing interruptions to protect member PHI/HIPAA information.

Nice To Haves

Master’s degree in Computer Science, Engineering, Artificial Intelligence, Information Systems, Data Science, or a related discipline.
At least 7 years of overall engineering experience, including designing enterprise-scale automation, integration, or AI solutions.
Mathematics, Statistics, or a related quantitative background is strongly preferred.
Experience leading AI automation, generative AI, agentic AI, or intelligent process automation initiatives in a large enterprise.
Experience in a regulated enterprise environment where security, privacy, auditability, governance, and responsible AI controls are material design requirements.
Relevant cloud, AI, automation, or software engineering certifications.

Responsibilities

Lead technical design and hands-on development of AI-enabled automations, agentic workflows, and intelligent process orchestration for enterprise Data Governance use cases.
Build solutions combining large language models, deterministic business logic, workflow engines, APIs, enterprise data, and human-in-the-loop controls.
Design reusable automation frameworks, services, connectors, prompts, tools, and patterns for multiple governance workflows.
Integrate AI automation with metadata management, data catalog, data quality, lineage, policy, ticketing, collaboration, and enterprise data platform capabilities.
Engineer APIs and services that enable agents and automations to safely retrieve context, execute approved actions, and interact with enterprise systems.
Use Power Platform and other low-code automation technologies when appropriate, applying software engineering discipline to maintainability, security, testing, and lifecycle management.
Establish patterns for agent orchestration, tool use, state management, exception handling, observability, auditability, human approval, and failure recovery.
Define and implement evaluation, testing, monitoring, and quality controls for AI automations, including accuracy, reliability, latency, cost, and operational performance.
Partner with architects, security, AI/data science teams, product owners, governance leaders, and application teams to translate business processes into secure, scalable automation solutions.
Lead technical reviews, mentor engineers, improve engineering standards, and provide technical direction while remaining involved in hands-on delivery.
Identify processes suited to automation and challenge unnecessary manual steps, handoffs, and duplicated workflows.
Own production readiness and operational support, including CI/CD, release management, telemetry, incident troubleshooting, continuous improvement, and responsible AI controls.