About the job
The GPS AI Catalyst Team (ACT) is dedicated to AI adoption across the public sector. By tightly aligning our engineering efforts with Go-to-Market and Google Public Sector Services, we incubate and productionalize differentiated AI solutions to solve repeatable mission-critical use cases. We serve as the technical backbone and trusted advisors for customers, ensuring the swift and secure integration of AI solutions, from initial concept through sustained deployment. As an AI/ML Engineer, you will be a key member of the Google Cloud Consulting Professional Service Organization, guiding customers through their business transformation by leveraging Google's AI technologies. You will design, prototype, and implement modern technical architectures. This role involves acting as a trusted technical advisor, resolving challenges, and sharing best practices through various forms of technical content.
Responsibilities
Be a trusted technical advisor to customers and solve complex machine learning challenges.
Create and deliver best practice recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of key business and technical stakeholders.
Work with customers, partners, and Google product teams to deliver tailored solutions into production.
Coach customers on the practical challenges in ML systems: feature extraction/feature definition, data validation, monitoring, and management of features/models.
Qualifications
Minimum
Bachelor's degree in Computer Science or equivalent practical experience.
6 years of experience building machine learning solutions and working with technical customers.
Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.
Experience with frameworks for deep learning (e.g., Tensorflow, Jax, PyTorch, Ray, etc.), AI accelerators (e.g., TPUs, GPUs), model architectures (e.g., encoders, decoders, transformers), and using machine learning APIs.
Must possess an active Top Secret/SCI security clearance with current polygraph.
Ability to travel up to 30% of the time.
Preferred
Experience with LLMs and LLM-based solutions such as prompt engineering, fine-tuning, RAG workflows, and agentic systems.
Experience in containerizing ML workloads within Linux/Unix environments.
Ability to lead the design and implementation of AI-based solutions, web services, and debugging tools.