Forward Deployed Engineer (FDE) - Applied AI

Capgemini•Atlanta, GA

1d•$82,082 - $193,440•Hybrid

About The Position

At Capgemini, you will collaborate with cross-functional teams to deliver innovative technology solutions that drive business value and enhance client experiences. You will contribute to the design, development, and continuous improvement of scalable, high-quality solutions in a dynamic and collaborative environment. As a Forward Deployed Engineer (FDE) in Applied AI, you are the "Agent Engineer" and the primary driver for our customers' most critical AI initiatives. You take initial conversational prototypes and transform them into production-ready solutions, owning the end-to-end engineering lifecycle, including the transition from "Art of the Possible" to real-world business value and scalable, secure AI systems. This is a high-travel, high-impact role focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers at their sites. This role requires a deep understanding of: Software Engineering Machine Learning Operations (MLOps) Cloud Infrastructure

Requirements

Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
5 years of experience with software development using Python or similar coding languages.
Experience architecting AI systems on cloud platforms (e.g., GCP).
Experience deploying resources via Terraform or similar tools to automate the setup of agents, functions, or networking.
Experience building full-stack applications that interact with enterprise IT infrastructures and developing external customer projects.

Nice To Haves

Master's or PhD in AI, Computer Science, or a related technical field.
Experience implementing multi-agent systems using frameworks like ReAct and self-reflection.
Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
Track record of troubleshooting live, high-traffic systems during critical windows.

Responsibilities

Serve as the lead developer for complex Conversational AI and CX applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI.
Architect and code conversational flows that are not just functional, but optimized for the "connective tissue" between Google's Conversational AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters.
Build high-performance evaluation (Eval) pipelines and observability frameworks to optimize complex agentic workloads, focusing on reasoning loops, tool selection, and reducing latency while maintaining production-grade security and networking.
Experience debugging Agent logic and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
Experience connecting agents to enterprise knowledge bases and optimizing RAG chunking to prevent hallucinations.
Build solutions that interact with enterprise IT infrastructures and support external customer projects.
Identify repeatable field patterns and technical "friction points" in Google's AAI stack, converting them into reusable modules or product feature requests for Engineering teams.
Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
Track record of troubleshooting live, high-traffic systems during critical windows.
Lead technical delivery for Conversational AI pilots and customer-facing implementations.