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Bachelor
Serve as a developer of complex reference solutions to enable customers to deploy Google’s latest and most advanced technologies.
Architect and develop reference prototypes being the connective tissue between Google’s advanced cloud solutions and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety and latency.
Identify repeatable field patterns and friction points within existing Google solutions, converting them into reusable modules or formal product feature requests for the Engineering teams.
Collaborate with Solution Architect teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
Minimum qualifications:
Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
5 years of experience building and shipping production-grade solutions to external or internal customers using Python, Typescript, or comparable languages.
Experience building pipelines for structured and unstructured data, incorporating vector databases and Retrieval-Augmented Generation (RAG) like architectures to power enterprise-grade AI solutions.
Experience leading technical discovery sessions with business stakeholders and engineering teams to define technology and hardware infrastructure requirements.
Experience in architecting technology solutions that ensure data sovereignty, GDPR compliance, and secure model governance.
Preferred qualifications:
Master’s degree or PhD in Computer Science, or a related technical field.
Experience architecting integrated systems, navigating real-time inference constraints, and implementing model quantization for resource-constrained environments.
Experience in optimizing state management and granular tracing, or to maximize throughput and minimize compute wastage with content generation at scale by leveraging ones knowledge of model serving metrics.
Experience in architecting and scaling production-grade ML systems in complex enterprise environments, workflow pipelines to implement CI/CD/CT automation and experimentation.
Experience with GenMedia models and fine-tuning capability to ensure hyper-realistic, brand-consistent content across image, video and audio.