AI Engineer, Google Cloud Consulting (English, French)
Google · Paris, Île-de-France, France
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- On site
- Full-time
- $150,000 / year
- Paris, Île-de-France, France
Job highlights
- Design and implement AI solutions for Google Cloud customers.
- Build classical ML and Generative AI applications.
- Work with cutting-edge Google AI technologies.
- Guide customers on productionizing AI systems.
- Collaborate with product teams and travel regionally.
About the role
About the job
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.
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).
Key skills/competency
- AI Engineering
- Machine Learning
- Generative AI
- Cloud Consulting
- Google Cloud Platform
- Python
- Deep Learning
- MLOps
- LLMOps
- Customer Engagement
Skills & topics
- AI Engineer
- Google Cloud
- Machine Learning
- Generative AI
- Cloud Consulting
- Python
- Deep Learning
- MLOps
- LLMOps
- Software Engineering
- Customer Facing
- French Language
- Vertex AI
- TensorFlow
- Dataflow
How to get hired
- Tailor your resume: Highlight your 3+ years of ML/GenAI experience, coding skills (Python, Java, etc.), and cloud solution design.
- Showcase customer focus: Emphasize experience with technical customers and stakeholder management, especially in English and French.
- Demonstrate cloud expertise: Detail your experience with Google Cloud products like Vertex AI and Dataflow, and MLOps/LLMOps.
- Prepare for technical interviews: Brush up on ML algorithms, deep learning frameworks, data engineering, and system design trade-offs.
- Practice communication: Be ready to discuss complex technical concepts clearly and concisely, demonstrating fluency in both English and French.
Technical preparation
Master Python, data structures, and algorithms.,Practice building classical and generative AI models.,Familiarize with Google Cloud AI services (Vertex AI).,Study MLOps/LLMOps principles and practices.
Behavioral questions
Describe a challenging ML project you completed.,How do you handle difficult customer technical issues?,Explain a complex AI concept simply.,How do you stay updated with AI advancements?
Frequently asked questions
- What are the minimum qualifications for the AI Engineer, Google Cloud Consulting role?
- The minimum qualifications include a Bachelor's degree in Computer Science or equivalent experience, 3 years of experience building and deploying machine learning solutions (Classical ML/Deep Learning and Generative AI), proficiency in programming languages like Python, experience designing cloud enterprise solutions, and fluency in both English and French for client relations.
- What specific AI technologies will I be working with as an AI Engineer at Google Cloud Consulting?
- You will leverage core Google products such as Vertex AI, foundation models like Gemini, TensorFlow, and Dataflow. This includes building classical ML pipelines and advanced Generative AI applications, and potentially working with RAG, vector databases, and orchestration frameworks.
- Does this AI Engineer role require travel?
- Yes, this AI Engineer, Google Cloud Consulting role requires travel up to 30% in-region for meetings, technical reviews, and onsite delivery activities to support client needs.
- What is the expected experience level for this AI Engineer position at Google?
- The role requires a minimum of 3 years of experience in building and deploying machine learning solutions, including classical ML/Deep Learning and Generative AI. Experience with cloud enterprise solutions and direct work with technical customers is also essential.
- What kind of customers will I be working with in this Google Cloud Consulting role?
- You will work directly with Google's most ambitious customers to identify high-impact opportunities, prototype AI solutions, and transition them into scalable production systems on Google Cloud.
- How important is fluency in French for this AI Engineer role?
- Fluency in both English and French is a minimum qualification for this AI Engineer, Google Cloud Consulting position, as it is required to support client relationship management in the specified region.
- What are MLOps and LLMOps, and how are they relevant to this AI Engineer job?
- MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations) are crucial for this role. You will guide customers on the practical challenges of production AI systems, which includes implementing best practices for MLOps and LLMOps, such as CI/CD for ML and model monitoring.