Automation Solution Lead

Role overview

AI architecture and systems thinking

• Design and orchestrate end-to-end GenAI and Agentic AI architectures using Google Cloud Platform, Vertex AI, and secure internal environments.
• Integrate AI agents into enterprise infrastructure while maintaining data integrity and security compliance.
• Create architecture designs and dataflow diagrams and lead solutions through architecture and security reviews.

Hands-on engineering and innovation

• Act as the primary hands-on engineering lead for complex GenAI applications and multi-agent frameworks.
• Write clean, standardized, reusable code.
• Assess the automation spectrum and determine where intelligent agents should enhance or replace traditional RPA.
• Use a customer-centric, design-thinking approach to prototype fact-based AI solutions.

Technical leadership and delivery

• Lead and mentor developers, business analysts, QA engineers, and offshore vendor partners.
• Establish development best practices and conduct code reviews for internal and vendor-delivered work.
• Maintain a standardized global code repository.
• Translate complex AI concepts, capabilities, and risks for non-technical business leaders.

Required experience

• Bachelor’s degree in computer science, data science, software engineering, or a related technical discipline.
• Three to five years of experience designing, building, and deploying enterprise-grade AI and automation solutions.
• Experience translating business requirements into scalable technical system designs.
• Ability to manage technical expectations, lead vendor teams, and troubleshoot complex workflow bottlenecks.

Preferred technical experience

• Cloud development experience is preferred, particularly with Google Cloud Platform and Vertex AI.
• Production experience with Agentic AI frameworks such as ADK, LangGraph, or similar is preferred.
• Experience with GenAI solutions such as retrieval-augmented generation and intelligent document processing is preferred.
• Advanced UiPath knowledge and understanding of RPA and large language model convergence is preferred.

Additional qualities

• A proactive, self-motivated approach to driving AI adoption across the organization.
• Demonstrated learning agility and willingness to master new technologies.

Position status