Applied AI Engineer

Google; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Google Cloud seeks an experienced AI/GenAI engineer to design and deploy agentic systems that scale for enterprise customers. You will build evaluation pipelines, optimize CUJs, and ensure production readiness across complex workflows.

The role emphasizes handling multilingual NLP models, calibrating tool selection, and maintaining security and performance during Go-Live windows. Travel up to 50% is possible.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 6+ years of software development with Python or similar languages.
• Experience designing AI systems on cloud platforms (GCP).
• Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or RAG systems.
• Experience developing and deploying multilingual NLP models.

Responsibilities

• Build high-performance evaluation pipelines and observability for agentic systems to ensure accuracy, safety, and latency.
• Own the delivery of Customer User Journeys (CUJs), ensuring conversational flows are production-ready for top priorities.
• Architect and optimize complex agent workloads, focusing on reasoning loops, tool selection, and reducing latency with secure networking.
• Implement critical infrastructure including rate limiting, error handling, and regional failover; develop monitoring and alerting for Go-Live windows.

Skills

Python

AI systems on cloud

GenAI solutions

Multilingual NLP models

Education

Bachelor's degree or equivalent

Tools

GCP

RAG workflows

Agent reasoning loops

Job description

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 6 years of experience with software development using Python or similar coding languages.
• Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., GCP).
• Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or RAG systems.
• Experience developing and deploying multilingual natural language processing models.

Preferred qualifications:

• Experience in debugging Agent logic (ReAct loops, Chain of Thought) and optimizing tool selection.
• Experience with troubleshooting live, high-traffic systems during critical windows.
• Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
• Ability to trace conversation IDs across microservices to identify and resolve failures in real-time.
• Ability to travel up to 50% as require.

About the job

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.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.
Spain: €90000 - €92000 (EUR) + 15% bonus target + equity + benefits
Learn more about benefits at Google.

Responsibilities

• Build high-performance evaluation (Eval) pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
• Own the delivery of the Customer User Journeys (CUJs), ensuring that conversational flows are not just functional, but optimized and production ready for (top customer/industry priorities).
• Architect and optimize complex agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking.
• Implement critical infrastructure including rate limiting, error managing, and regional failover strategies; develop monitor systems and alerting dashboards during "Go-Live" windows to ensure stability as traffic scales.
• Travel up to 50% of the time.