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Open nowFirst seen 18 hours ago

AI Innovation Engineer, Google Cloud

Google3,572 open roles

Where
Frankfurt am Main, Germany; Zürich, Switzerland; Munich, Germany; London, UK
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Your applicationOpen nowAI Innovation Engineer, Google CloudGoogle · Frankfurt am Main, Germany; Zürich, Switzerland; Munich, Germany; London, UK

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Google's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

9.8% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 28 days.

Share of postings closed within
  1. 1.9%1 day
  2. 3.9%3 days
  3. 9.8%7 days
  4. 15.6%14 days
  5. 33.9%30 days
This job: first seen 18 hours ago

Google median: 28 days open

The posting

In most instances, this position requires in-person interviews as part of the hiring process. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Frankfurt am Main, Germany; Zürich, Switzerland; Munich, Germany; London, UK.

Minimum qualifications:

  • Bachelor's degree in Computer Science, a related field, or equivalent practical experience.
  • 8 years of experience in software or data engineering, including one or more programming languages (e.g., Python, Go, Java), and with design patterns, testing frameworks, and API contract design.
  • Experience using machine learning methodologies (deep learning, reinforced learning), model identification, selection and AI operations (e.g., model monitoring).
  • Experience using Generative AI and agentic orchestration utilizing frameworks (e.g., LangChain, CrewAI, or Vertex AI Agent Builder) and vector databases.

Preferred qualifications:

  • Experience in financial services, and with the regulatory and operational clearing, settlement, or custody.
  • Experience with FSI regulatory practices and data residency, encryption at rest/transit (CMEK), and "explainable AI" requirements in banking.
  • Experience with data modeling of relational, NoSQL, and analytical data modeling (Star Schema, Data Vault).
  • Experience in BigQuery, Vertex AI, Dataflow, and Pub/Sub with an ability to drive the discovery phase, moving from a vague business problem to a structured product requirement document (PRD) and a working technical demo.
  • Experience working in a high-maturity DevOps culture (e.g., trunk-based development, automated testing, blue/green deployments).

About the job

In this role, you will accelerate customer value and increase adoption by delivering innovative, repeatable, and enterprise-ready solutions focused on business value. Make Google Cloud the preferred choice for customers by delivering the highest-value, industry relevant solutions.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge 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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Germany: €150000 - €153000 (EUR) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Design and build autonomous agentic workflows utilizing Machine Learning and generative AI technologies as part of a fully autonomous or human-in-the-loop agentic workflow.
  • Partner with client leads (business user) to identify high-impact AI use cases. Translate these into product requirement documents (PRDs), clearly defining critical user journeys (CUJs) and success metrics.
  • Evaluate and integrate AI solutions with modern data foundations, including relational databases, data lake houses, and real-time streaming architectures.
  • Ensure all prototypes are built with a "production-first" mindset. Implement basic CI/CD pipelines and utilize infrastructure-as-code (IaC) (e.g., Terraform) to ensure environments are reproducible and secure.
  • Create clear technical guides to ensure a seamless hand-off from proof-of-concept (POC) to engineering teams.
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