Forward Deployed Engineer, GenAI, Google Cloud
Google · Paris, Île-de-France, France
- On site
- Full-time
- $150,000 / year
- Paris, Île-de-France, France
Job highlights
- Build production-grade AI solutions for customers.
- Integrate frontier AI products into customer environments.
- Manage AI production blockers and data readiness.
- Provide feedback to shape Google Cloud's roadmap.
- Collaborate with DeepMind engineers and researchers.
About the role
Forward Deployed Engineer, GenAI
Google welcomes people with disabilities.Minimum qualifications
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- Experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, TypeScript, or similar languages.
- Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
- Experience architecting scalable AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
- Experience building pipelines for structured, unstructured data, incorporating vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise-grade AI solutions.
Preferred qualifications
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s ADK) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of LLM-native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
About the job
As a GenAI Forward Deployed Engineer at Google Cloud, you will be an embedded builder bridging the gap between frontier AI products and production-grade reality for our customers. You will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.In this role, you will manage blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white-glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.
It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Responsibilities
- Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable return on investment.
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and technical friction points in Google’s AI stack, converting them into reusable modules or product feature requests for the Engineering teams.
- Drive engineering excellence by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .
Key skills/competency
- Generative AI
- Cloud Computing
- Python
- TypeScript
- Machine Learning
- Data Pipelines
- Vector Databases
- RAG
- Agentic Systems
- Production Deployment
Skills & topics
- Forward Deployed Engineer
- GenAI
- Generative AI
- AI Engineer
- Google Cloud
- GCP
- Python
- TypeScript
- Cloud Computing
- Machine Learning Engineer
- Data Pipelines
- Vector Databases
- RAG
- Agentic Systems
- Production Deployment
- Software Engineer
How to get hired
- Tailor your resume: Highlight your experience with Python, TypeScript, AI-driven solutions, and cloud platforms like GCP. Emphasize production-grade deployments and data pipeline expertise.
- Showcase AI system architecture: Detail your experience in architecting scalable AI systems, including RAG and vector databases, for enterprise-grade solutions.
- Demonstrate leadership and collaboration: Provide examples of leading technical discovery sessions and co-building with customer teams.
- Prepare for technical interviews: Be ready to discuss AI system design, debugging production issues, and optimizing LLM performance.
- Understand Google's culture: Research Google Cloud's commitment to AI innovation and its collaborative approach with DeepMind.
Technical preparation
Master Python/TypeScript for AI solution development.,Practice architecting scalable AI systems on GCP.,Build RAG pipelines with vector databases.,Familiarize with multi-agent system frameworks.
Behavioral questions
Describe a complex AI integration you solved.,How do you manage production AI system issues?,Explain how you'd gather customer technical requirements.,How do you provide feedback for product improvement?
Frequently asked questions
- What is a Forward Deployed Engineer role at Google Cloud focused on GenAI?
- A GenAI Forward Deployed Engineer at Google Cloud acts as an embedded builder, connecting cutting-edge AI products with production realities for customers. This role involves coding, debugging, and deploying custom agentic AI solutions directly within customer environments, managing integration and data complexities to achieve enterprise-grade AI maturity.
- What technical skills are essential for the Forward Deployed Engineer, GenAI position at Google?
- Essential technical skills include proficiency in Python or TypeScript, experience building production-grade AI solutions, architecting scalable AI systems on cloud platforms (especially GCP), and developing data pipelines with vector databases and RAG architectures. Experience with multi-agent systems and LLM optimization is also highly valued.
- How does this role contribute to Google Cloud's product development?
- As an embedded builder, you provide a critical feedback loop by identifying real-world technical challenges and patterns encountered during customer deployments. This field insight is transformed into actionable feedback for Google Cloud's engineering teams, directly influencing the future product roadmap.
- What is the work arrangement for a Forward Deployed Engineer, GenAI at Google?
- This role involves being embedded directly within customer environments to deploy AI solutions. While the exact arrangement can vary based on customer needs, it emphasizes hands-on, on-site or near-site collaboration to ensure seamless integration and successful deployment.
- What are the expected career growth opportunities for a GenAI Forward Deployed Engineer at Google?
- This role offers significant growth opportunities by being at the forefront of AI innovation, working with frontier models like Gemini and the Vertex AI platform. You gain deep expertise in deploying complex AI systems, influencing product roadmaps, and collaborating with leading AI researchers, positioning you for leadership in the AI revolution.
- How can I best prepare my resume for the Forward Deployed Engineer, GenAI role at Google?
- Tailor your resume to highlight specific achievements in building and shipping production-grade AI solutions, experience with Python/TypeScript, cloud architecture (GCP), and data pipeline development (RAG, vector databases). Quantify your impact wherever possible, especially regarding ROI and system performance.
- What kind of interview questions can I expect for the Forward Deployed Engineer, GenAI position?
- Expect a mix of behavioral and technical questions. Technical interviews will likely cover AI system design, cloud infrastructure, data processing, debugging challenges, and your approach to production deployment. Behavioral questions will assess your problem-solving, collaboration, and communication skills.