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# Forward Deployed Engineer - Enterprise AI Solutions

- **Company:** Rackspace
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $165,830.0 - $243,142.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Databases, Information Engineering, DevOps, Graph Database, Python (Programming Language), Rapid Prototyping Process, Software Deployment, Real Time Systems, Large Language Models, Multi-Agent Systems, Data Lakes, Kubernetes, Information Technology, Data Pipelines, Docker
- **Published:** May 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=090a6348be055423

## About the Role

Do you have experience in Scalable systems?, Do you have a Bachelor's degree?, * Bachelor's degree in Computer Science, Data Science, or equivalent practical experience.

 * 7+ years of professional software, data engineering, or AI/ML delivery experience.
 * 4+ years of experience in a customer-facing or professional services engineering capacity.
 * Deep technical experience with Python, enterprise-scale AI/ML, and DevOps workflows (Docker, Kubernetes).
 * Willingness to travel up to 25% for customer engagements.

## Description

As a Forward Deployed Engineer at Rackspace Technology, you will be embedded directly with our most strategic enterprise customers to architect, build, and deploy high-impact AI solutions. This role combines deep technical engineering with business acumen, customer empathy, and end-to-end solution ownership. You become the technical bridge between Rackspace's AI platform capabilities and the customer's most pressing business challenges. You will own the full solution lifecycle from problem discovery and rapid prototyping through production deployment and continuous optimization., * Embed with strategic enterprise customers to rapidly diagnose business challenges, map data landscapes, and co-design AI solutions on-site.

 * Lead end-to-end solution design and delivery of agentic AI workflows, RAG pipelines, knowledge graphs, and real-time applications.
 * Drive rapid prototyping and proof-of-concepts (POCs) demonstrating value within days or weeks.
 * Act as primary technical owner across the full project lifecycle: scoping, architecture, build, deployment, and post-launch optimization.
 * Build scalable data pipelines across structured and unstructured formats using vector databases.
 * Develop, optimize, and fine-tune LLM/SLM models and multi-agent systems.
 * Integrate solutions across heterogeneous enterprise architectures (ERP, CRM, databases, data lakes).
 * Feed customer insights back to internal Platform and Product teams to scale future feature rollouts.


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