Forward Deployed Engineer (FDE) - Applied AI

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Location Hybrid work from Chicago, Atlanta, Nashville, Dallas, NY About the job you're considering 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. Role Overview 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 Key Responsibilities Conversational AI & Agent Engineering • 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. Evaluation, Optimization & Observability • 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. Platform Integration & Knowledge Systems • 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. Product Feedback & Engineering Excellence • 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. Production Systems & Customer Delivery • Track record of troubleshooting live, high-traffic systems during critical windows. • Lead technical delivery for Conversational AI pilots and customer-facing implementations. Minimum Qualifications Education • Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience. Software Development • 5 years of experience with software development using Python or similar coding languages. Cloud & Infrastructure • 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. Application Development • Experience building full-stack applications that interact with enterprise IT infrastructures and developing external customer projects. Preferred Qualifications Advanced Education • Master's or PhD in AI, Computer Science, or a related technical field. Agentic AI & Multi-Agent Systems • Experience implementing multi-agent systems using frameworks like ReAct and self-reflection. AI Optimization & Debugging • 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. Production Support • Track record of troubleshooting live, high-traffic systems during critical windows. #LI-SS1 The base compensation range for this role in the posted location is $82,082 to $193,440 Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law. The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction. These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity. It is not typical for candidates to be hired at or near the top of the posted compensation range. In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws. Capgemini offers a comprehensive, n