Forward Deployed Engineer III, Applied AI

As a Forward Deployed Engineer in Applied AI, you drive the end-to-end transformation of conversational AI prototypes into production-ready solutions. You lead delivery for customer journeys, optimize agent workloads, and implement secure, scalable infrastructure during go-live. You work with cross-functional teams to balance latency, accuracy, and cost while satisfying security requirements. This role blends hands-on engineering with strategic delivery to accelerate customer value with Gemini Enterprise and other AI solutions.

• Lead delivery for conversational AI pilots and productionize PoC code with real integrations
• Own end-to-end Customer User Journeys (CUJs) and optimize conversational flows for top priorities
• Architect and optimize agentic workloads, including reasoning loops and tool selection, to reduce latency
• Implement critical infrastructure: rate limiting, error handling, regional failover, monitoring, and Go-Live readiness
• Design security perimeters (VPC, IAM, CMEK) to meet CISO requirements and enable secure RAG deployments
• Balance latency, accuracy, and cost through optimized RAG strategies and scalable architecture
• Develop monitoring dashboards and alerts for stable, scalable traffic during Go-Live windows
• Collaborate with model builders and cross-functional teams to deliver fast, reliable AI solutions
• Engage with enterprise knowledge bases and multi-agent frameworks to reduce hallucinations

• Bachelor’s degree or equivalent practical experience
• 2 years of software development experience in Python or C++
• 2 years of GenAI techniques or related concepts (LLMs, multi-modal, large vision models)
• 1 year of AI/ML infrastructure experience (deployment, evaluation, optimization, data processing, debugging)
• Experience developing and deploying multilingual natural language processing models

• Versatility and leadership
• Collaborative mindset across cross-functional teams
• Problem-solving and proactive communication
• Python or C++ programming
• GenAI techniques (LLMs, multi-modal, LVMs)
• AI/ML infrastructure (model deployment, evaluation, optimization)