Overview
The primary responsibility of this role is to act as a GCP Agentic Product Engineer for medium-to-large AI transformation opportunities, supporting mid-sized clients in designing reusable AI-enabled assets, engineering cloud-native AI products, developing agentic workflows, and establishing scalable deployment frameworks on Google Cloud Platform (GCP). The role focuses on creating reusable, commercially viable AI solutions that accelerate time-to-value, improve business outcomes, and can be rapidly deployed across multiple client engagements.
The ideal candidate will leverage strong product engineering, cloud development, and AI expertise to build scalable agentic AI assets, collaborate with architects and business stakeholders, establish engineering standards, and ensure high-quality product delivery. Working closely with Sales, Architecture, Engineering, Delivery, and Product teams, the engineer will combine software engineering, cloud infrastructure, automation, testing, and AI innovation to deliver differentiated AI-native solutions. Key Responsibilities Translate business and technical requirements into reusable AI-enabled products using Google Cloud services and modern engineering practices.
Collaborate with Sales, Solution Architects, Engineering, and Delivery teams to develop scalable and repeatable AI offerings.
Design and build reusable agentic AI assets that accelerate deployment and adoption across client engagements.
Define product architecture standards, engineering frameworks, reusable design patterns, and deployment approaches.
Develop proof-of-concepts, pilot solutions, packaged AI assets, and reusable accelerators.
Ensure solutions meet scalability, security, reliability, maintainability, and operational excellence standards.
GCP Architecture & Cloud Engineering
Design reusable agent configurations using Vertex AI Agent Builder, orchestration frameworks, and AI workflow architectures.
Develop Infrastructure-as-Code templates, CI/CD pipelines, and deployment accelerators using Google Cloud services.
Create reference architectures supporting agentic AI, intelligent automation, and AI-native applications.
Integrate AI products with enterprise systems, APIs, cloud services, and data platforms.
Optimize architectures for reusability, maintainability, performance, and deployment efficiency.
Engineering Excellence & Quality
Develop testing, validation, evaluation, monitoring, and quality assurance frameworks for AI-enabled products.
Implement automated testing and deployment practices to ensure product reliability and consistency.
Establish engineering standards, coding guidelines, governance frameworks, version control, release management, and continuous improvement processes.
Improve engineering productivity through automation and reusable development assets.
Client Engagement & Collaboration
Partner with architects, product owners, delivery teams, and business stakeholders to align solutions with business objectives.
Drive AI product innovation, reusable asset development, and engineering best practices.
Mentor engineering teams on GCP engineering, AI product development, and agentic architecture.
Contribute to reusable frameworks, accelerators, thought leadership, and continuous capability development.
Qualifications
Experience
5–8 years of experience in Product Engineering, Software Engineering, Cloud Engineering, AI Engineering, or related fields.
Experience designing cloud-native applications and reusable software assets on Google Cloud Platform.
Hands-on experience with AI, machine learning, automation, intelligent workflows, Infrastructure-as-Code, CI/CD, and automated deployment frameworks.
Proven ability to translate business requirements into scalable product architectures and develop proof-of-concepts, reusable accelerators, testing frameworks, and productized assets.
Experience supporting enterprise AI transformation and cloud modernization initiatives.
