The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.
As a Cloud AI Engineer, you will design, prototype, and implement state-of-the-art AI solutions for customer use cases. In this role, you will act as an ML generalist, bridging the gap between research and enterprise production. You will leverage core Google products, including Vertex AI, our latest foundation models (Gemini), TensorFlow, and Dataflow to build both classical machine learning pipelines (e.g., predictive modeling, forecasting, clustering) and advanced Generative AI applications.
You will work directly with our most ambitious customers to identify high-impact opportunities, rapidly prototype solutions, and transition those prototypes into scalable production systems. You will support customer implementation through architecture guidance, system design, MLOps/ Large Language Model Operations (LLMOps) best practices, capacity planning, and coding. Additionally, you will work closely with Product Management and Product Engineering to share field insights and constantly drive excellence in AI portfolios.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Advise customers as a trusted technical partner to solve technical challenges, anticipating issues before they arise and offering a breadth of scalable solutions and trade-offs.
- Write clean, well-structured, production-ready code to integrate classical ML models and Generative AI into enterprise environments.
- Guide customers on the practical challenges of production AI systems, spanning traditional ML (feature extraction, data validation, model tuning, and evaluation) and GenAI (prompt engineering, model evaluation, fine-tuning, and LLMOps).
- Collaborate with Customers, Partners, and Google Product teams to design real-world, practical systems, shifting customized AI prototypes into highly reliable, scalable production architectures on Google Cloud.
- Travel up to 30% in-region for meetings, technical reviews, and onsite delivery activities.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science or equivalent practical experience.
- 3 years of experience building and deploying machine learning solutions (including both Classical ML/Deep Learning and Generative AI) and working directly with technical customers or stakeholders.
- 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 designing cloud enterprise solutions and supporting customer projects to completion.
- Ability to communicate in English and French fluently to support client relationship management in this region.
Preferred qualifications:
- Experience building Generative AI applications, including working with foundation models, Retrieval-Augmented Generation (RAG), vector databases, and orchestration frameworks.
- Experience with deep learning frameworks (e.g. TensorFlow, PyTorch, XGBoost).
- Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g. Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).
- Knowledge of data engineering concepts, distributed data pipelines, and infrastructure tools (e.g., Apache Beam, Hadoop, Spark, BigQuery).
- Understanding of real-world system design, trade-offs, and the auxiliary practical concerns in productionizing AI systems (MLOps, LLMOps, CI/CD for ML, model monitoring).