Applied AI Engineer

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

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.

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 handling, and regional failover strategies; develop monitor systems and alerting dashboards during "Go-Live" windows to ensure stability as traffic scales.
• Drive product feedback loops based on deployment experiences and contribute to the broader community through whitepapers, hackathons, and technical blogs.