Forward Deployed Engineer, Applied AI, Google Cloud

Google
Zürich, Switzerland
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Zürich, Switzerland

Tech stack

API
Artificial Intelligence
Cloud Computing
Software Debugging
IT Management
Intrusion Detection Systems
Python
Software Engineering
Google Cloud Platform
Chatbots
React
Multi-Agent Systems
Generative AI
Information Technology
Machine Learning Operations
Terraform
Programming Languages
Microservices

Job description

As a Forward Deployed Engineer (FDE) in Applied AI, you are the "Agent Engineer" and the primary driver for our customers' most critical AI initiatives. You take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering life-cycle, including the transition from "Art-of-the-Possible" to real-world business value and scalable, secure AI systems. In this high-travel, high-impact role, you will be focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers at their sites. You will require a deep understanding of software engineering, machine learning operations, and cloud infrastructure.It's an exciting time to join Google Cloud's Go-To-Market team, leading the AI revolution for businesses worldwide. You'll leverage 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 complex Conversational AI and CX 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 conversational flows that are not just functional, but optimized for the "connective tissue" between Google's Conversational AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters.
  • Build high-performance evaluation (Eval) pipelines and observability frameworks to optimize complex agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking.
  • Identify repeatable field patterns and technical "friction points" in Google's AAI stack, converting them into reusable modules or product feature requests for Engineering teams.
  • Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Requirements

Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain., * Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.

  • 8 years of experience with software development using Python or similar coding languages.
  • Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP).
  • Experience deploying resources using Terraform or similar tools, to automate the setup of agents, functions, or networking.
  • Experience building full-stack applications that interact with enterprise IT infrastructures, and developing external customer projects.

Preferred qualifications:

  • Master's or PhD in AI, Computer Science, or a related technical field.
  • Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.
  • Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
  • Experience connecting agents to enterprise knowledge bases and optimizing Retrieval-augmented generation (RAG) chunking to prevent hallucinations.
  • Ability to travel up to 50% of the time.
  • Track record of troubleshooting live, high-traffic systems during critical windows.

About the company

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See alsoGoogle's EEO Policy (https://www.google.com/about/careers/applications/eeo/) ,Know your rights: workplace discrimination is illegal (https://careers.google.com/jobs/dist/legal/EEOC_KnowYourRights_10_20.pdf) ,Belonging at Google (https://about.google/belonging/) , andHow we hire (https://careers.google.com/how-we-hire/) . If you have a need that requires accommodation, please let us know by completing ourAccommodations for Applicants form (https://goo.gl/forms/aBt6Pu71i1kzpLHe2) . Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting. To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.

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