Lead AI Automation Engineer

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The Lead AI Automation Engineer will provide hands-on technical leadership for the design, engineering, and productionization of AI-enabled automation across the Data Governance organization. This role is focused on building reliable automation products and agentic workflows—not training foundational models or serving as a traditional data scientist. The engineer will combine generative AI, workflow orchestration, APIs, enterprise integrations, low-code automation where appropriate, and production engineering practices to automate complex governance processes.

The role will lead technical design, establish reusable engineering patterns, mentor other engineers, and take high-value automation opportunities from concept through secure, scalable production deployment.

Key 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 that combine 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 that accelerate delivery across 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 allow 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 they are the appropriate engineering choice, while 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 and scalable automation solutions.

• Lead technical reviews, mentor engineers, improve engineering standards, and provide technical direction without becoming removed from hands-on delivery.

• Identify processes that are strong candidates 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.

Use your skills to make an impact

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, Engineering, Data Science, Artificial Intelligence, or a related technical field; or equivalent combination of education and relevant professional experience.

• 5+ years of experience in software engineering, application development, automation engineering, platform engineering, or enterprise systems integration.

• 2+ years of hands-on experience developing AI/ML, generative AI, intelligent automation, or advanced automation solutions, with demonstrated recent experience building LLM-enabled or agentic solutions.

• Demonstrated experience taking complex automation or AI-enabled solutions from concept/prototype through production deployment and operational support.

• Demonstrated experience providing technical leadership—such as leading solution design, establishing engineering patterns, conducting technical/code reviews, mentoring engineers, or serving as the technical lead for complex initiatives.

Preferred Qualifications

• Master's degree in Computer Science, Engineering, Artificial Intelligence, Information Systems, Data Science, or a related discipline.

• 7+ years of overall engineering experience, including experience designing enterprise-scale automation, integration, or AI solutions.

• Strong preference for a Mathematics, Statistics, or related quantitative background

• Experience leading AI automation, generative AI, agentic AI, or intelligent process automation initiatives in a large enterprise.

• Experience working in a regulated enterprise environment where security, privacy, auditability, governance, and…