Sr Forward Deployed Engineer

## Sr Forward Deployed Engineer Apply locations:
US-Work from Hometime type:
Full time posted on:
Posted Todayjob requisition :
R-23628
** Job Summary**:
As a Sr. Forward Deployed Engineer (Sr. FDE) 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 while feeding field insights back to our product and platform engineering teams. This role is ideal for someone who thrives at the intersection of engineering, strategy, and customer engagement and wants the autonomy and impact typically found at an AI startup, backed by the scale and resources of a global technology company.
** Work Location/Travel:
*** If located in San Antonio, TX you’ll work a hybrid schedule with 2 days in our office, and three days remotely.
* If located outside of San Antonio, TX you may work 100% remotely.
* Willingness to travel up to 25% for on-site customer engagements.
*
* Key Responsibilities:

*** Diagnose critical 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 decision-making applications.
* Drive rapid prototyping and POCs that demonstrate tangible business value within days to weeks.
* Serve as the primary technical owner across the full project lifecycle: scoping, architecture, build, deployment, and post-launch optimization.
* Architect production-grade Enterprise AI applications on Partner Foundry Solutions or Rackspace Private Cloud and GPU infrastructure, integrating with enterprise systems (ERP, CRM, data warehouses, data lakes).
* Build scalable data pipelines across structured and unstructured data using ETL/ELT, vector databases (Pinecone, Weaviate, AstraDB), and knowledge base frameworks.
* Develop and fine-tune LLM/SLM solutions; implement RAG architectures (Llama Index, Haystack) and orchestrate multi-agent workflows (Lang Chain, Lang Graph, CrewAI).
* Ship with full-stack and Dev Ops depth:
Python, Node.js/Go, React/Vue, Docker, Kubernetes, CI/CD, and GPU cluster management.
* Champion observability, monitoring, and telemetry to ensure trustworthy, auditable, and versioned AI agents in production.
* Identify expansion opportunities by working with sales and customer success to uncover high-value use cases across new business domains.
* Feed structured field insights back to Platform Engineering and Product on feature gaps, emerging needs, and usability improvements.
* Build reusable IP through reference architectures, accelerators, frameworks, and technical best practices that scale future engagements.
* Mentor engineers and customer teams, driving knowledge transfer and building internal AI competencies.
*
* Required Qualifications:

*** Bachelor’s degree in computer science, engineering, or related technical discipline required. Additional experience may substitute for the degree.
* Must be Palantir certified.
* 10+ years in software engineering, data engineering, or AI/ML delivery; at least 4+ years in customer-facing or field roles.
* Proven track record in building and deploying AI/ML applications in production at enterprise scale.
* Deep full-stack proficiency:
Python (required), Node.js/Go, React/Vue, SQL/No

SQL databases.
* Hands-on with LLMs, prompt engineering, vector databases, data pipelines, application dashboards, RAG pipelines, and agent orchestration frameworks.
* Strong Dev Ops skills:
Docker, Kubernetes, CI/CD, GPU infrastructure, cloud-native deployment patterns.
* Experience integrating across heterogeneous enterprise systems - ERP, data warehouses, data lakes, streaming architectures.
* Ability to translate ambiguous customer needs into actionable engineering plans under…