Organize the deployment of AI workflows and architectures across diverse AI and technology platforms. Facilitate solution design across enterprise technology stack to integrate into the ecosystem of AI models and multi provider platforms.
Responsibilities
Embed directly with client infrastructure and engineering teams to design, execute, and operationalize end-to-end transformation — spanning cloud foundation, hyperconverged infrastructure, platform engineering, application modernization, database transformation, SAP, network and security, and AI platform deployment.
Accountable for production outcomes — platform reliability, time-to-value, adoption, and scalability — measured against business metrics, not project milestones.
Move from ambiguous infrastructure problems to working production environment through rapid iteration: days to validated architecture, weeks to production-ready deployment.
Execute application migration hands-on — rehost (lift & shift), re-platform (lift & optimize), and redeploy — applying the right strategy per workload to land on an AI-ready, modernized target platform.
Architect and implement network and security foundations: Zero Trust, SASE/SSE, micro-segmentation, container security, and application AI governance across enterprise environments.
Requirements
15 years full time education
Nice to have: Cloud Automation DevOps
Nice to have: Cloud Strategy and Assessment
Nice to have: Docker Kubernetes Architecture
Forward Deployed Engineer
Accenture Infrastructure and Capital Projects, LLC · Posted Nov 3, 2026
A Forward Deployed Infrastructure Transformation Engineer is a production engineer working embedded inside a client's enterprise to transform legacy infrastructure and Application estates into AI-ready, cloud/Hybrid platforms and Modernized Infrastructure, focusing on time-to-value, reliability, adoption, and scalability.
Responsibilities
Organize the deployment of AI workflows and architectures across diverse AI and technology platforms.
Facilitate solution design across enterprise technology stack to integrate into the ecosystem of AI models and multi provider platforms.
Embed directly with client infrastructure and engineering teams to design, execute, and operationalize end-to-end transformation.
Accountable for production outcomes — platform reliability, time-to-value, adoption, and scalability.
Move from ambiguous infrastructure problems to working production environment through rapid iteration.