AI Innovation Engineer, Google Cloud

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Frankfurt am Main, Germany; Munich, Germany; London, UK.

Minimum qualifications:

Bachelor's degree in Computer Science, a related field, or equivalent practical experience.

6 years of experience in software or data engineering.

Experience solving real-world problems using machine learning methodologies (deep learning, reinforced learning), model identification, selection and AI operations (e.g., model monitoring).

Experience solving real-world problems using Generative AI and agentic orchestration utilizing frameworks (e.g., LangChain, CrewAI, or Vertex AI Agent Builder) and vector databases.

Experience with one of the following programming languages: Python, Go, or Java.

Experience with design patterns, testing frameworks, and API contract design.

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, etc.).

Experience working in a high-maturity DevOps culture (trunk-based development, automated testing, blue/green deployments).

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 PRD and a working technical demo.

About the Job

The Global Solutions and Consumer AI team accelerates 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.

In this role, you will collaborate with clients to transform initial concepts into reality. You will create solutions using the latest machine learning and Generative AI technologies to replace traditional workflows for a customer in the capital markets finance industry. You will work at the heart of Financial Market Infrastructures (FMIs) including high-stakes clearing, settlement, and custody environments to identify where AI can drive systemic efficiency.

You will act as a motivated innovator and builder who can operate with minimal guidance. You will not only code and create functional prototypes but also help define the Critical User Journeys (CUJs) and technical requirements that turn a Proof-of-Concept (POC) into a scalable, production-ready solution usable across a broad base of Google Cloud Platform (GCP) clients.

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: €124000 - €128000 (EUR) + 15% bonus target + equity + benefits

Learn more about benefits at Google.* 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 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 POC to engineering teams.