Lead AI Automation Engineer

Humana•Tampa, FL

1d•$129,300 - $177,800•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 focuses on building reliable automation products and agentic workflows rather than training foundational models or performing traditional data science. It combines generative AI, workflow orchestration, APIs, enterprise integrations, low-code automation where appropriate, and production engineering practices to automate complex governance processes. The engineer leads technical design, establishes reusable engineering patterns, mentors other engineers, and takes high-value automation opportunities from concept through secure, scalable production deployment. The role includes integrating automation with enterprise governance and data capabilities, creating safe ways for agents to retrieve context and take approved actions, and establishing controls for orchestration, evaluation, monitoring, auditability, human approval, and recovery. The engineer partners across architecture, security, AI and data science, product, governance, and application teams, while remaining actively involved in delivery and production operations. This is a remote position. Occasional travel to Humana offices for training or meetings may be required. Home or hybrid home/office employees must have internet service with at least 25 Mbps download and 10 Mbps upload speeds and work from a dedicated space that protects member PHI/HIPAA information. The position is scheduled for 40 hours per week. The stated starting base-pay range is $129,300–$177,800 per year, with eligibility for a bonus incentive plan.

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 demonstrated recent experience building LLM-enabled or agentic solutions.
Demonstrated experience taking complex automation or AI-enabled solutions from concept or prototype through production deployment and operational support.
Demonstrated 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.

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 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 the 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 where appropriate, applying software engineering practices for 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 suitable for 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.