Solutions Architect, Forward Deployed

Job Summary As a Solutions Architect, Forward Deployed at Rackspace Technology, you sit at the front of the sales motion and win business by building. You embed directly with our most strategic enterprise customers to diagnose high-value business problems, co-design AI solutions on-site, and prove them out with working prototypes; not slideware. This is a presales role for someone with an engineering and problem-solving mindset: you own the technical win, and you get there by shipping demonstrable value in days and weeks rather than quarters. You are platform-first. Your primary craft is enterprise AI development platforms; Palantir Foundry and AIP, and AWS Bedrock/Agent Core, deployed on Rackspace Private Cloud, GPU infrastructure, or the customer's AWS cloud. Cloud services are the substrate you build on, not the product you lead with. The role combines deep technical engineering with commercial instinct, customer empathy, and end-to-end ownership of the technical narrative from first conversation through signed Statement of Work and delivery handoff. It suits someone who wants the autonomy and immediacy of an AI startup, backed by the scale, partner relationships, and delivery bench of a global technology company. Work Location and Travel Candidates who live in the San Antonio, TX area work a hybrid schedule with two days in the office. If not located near San Antonio, you may work 100% from home. Travel up to 50% per business requirements, for on-site customer engagements, workshops, and partner and hyperscaler events. "Work from home postings are limited to candidates residing within the country specified in the posting location." Sponsorship This role is not sponsorship eligible. Candidates need to be legally allowed to work in the US for any employer. Key Responsibilities Opportunity Creation and Presales Leadership Drive top-of-funnel opportunity creation with sales, account teams, and partner alliances — opening executive conversations with working demonstrations of what is possible on our platforms. Lead on-site discovery: diagnose critical business challenges, map the customer's data landscape, and co-design AI solutions with business and technical stakeholders in the room. Own the technical narrative across the sales cycle; art-of-the-possible sessions, architecture workshops, platform evaluations and bake-offs, security and governance deep dives, and executive briefings. Translate customer business and IT needs into Statements of Work and Scope that delineate the need into a set of actionable, estimable work packages. Develop business cases and ROI models that connect AI ambition to the data foundation work required to support it, so the commercial case survives procurement scrutiny. Identify expansion opportunities across new business domains, working with sales and customer success to surface high-value use cases in accounts already live. Hand off to delivery cleanly;architecture, assumptions, dependencies, risks, and success criteria documented; and stay engaged as technical advisor through mobilization. Solution Design, Prototyping, and Demonstration Build rapid proofs of concept and working prototypes that demonstrate tangible business value within days to weeks. Design and demonstrate agentic AI workflows, RAG pipelines, knowledge graphs, and real-time decision-making applications against the customer's own data wherever possible. Architect production-grade enterprise AI applications on partner platforms, Rackspace Private Cloud, and GPU infrastructure, integrating with enterprise systems including ERP, CRM, data warehouses, and data lakes. Design ontologies and semantic models that make heterogeneous enterprise data usable by AI agents and operational workflows. Build demonstration data pipelines across structured and unstructured sources using ETL/ELT patterns, vector databases, and knowledge base frameworks. Build prototypes with enough engineering discipline to survive customer security and architecture review — versioned, observable, auditable, and evaluated. Maintain and extend a library of reusable demo assets, accelerators, and sandbox environments so the next pursuit starts further down the field. Technical Depth: Platform First, Cloud Second Palantir Foundry and AIP — maintain deep, current, hands-on expertise across data modeling, pipeline development, ontology design, operational workflows, and the build, deployment, and governance of AI-driven applications and decision intelligence solutions. AI and LLM engineering — prompt engineering, fine-tuning, model distillation, RAG implementation (LlamaIndex, Haystack), multi-agent orchestration (LangChain, LangGraph, CrewAI), vector databases (Pinecone, Weaviate, AstraDB), and AI evaluation frameworks. AWS as deployment substrate — working fluency in the AWS AI and data services that underpin platform deployments: Amazon Bedrock, Amazon Q, SageMaker including Unified Studio, AWS Glue, Redshift, EMR, QuickSight with Q, plus Databricks on AWS, Snowflake on AWS, Apache Iceberg, and Delta Lake. Full-stack and DevOps — Python, Node.js/Go, React/Vue, Docker, Kubernetes, CI/CD, and working familiarity with GPU cluster management — enough to build and stand up your own demos without waiting on a delivery team. Architectural judgment — guide build versus buy decisions across both AI capabilities and data platform components, and stay current on foundation models, agentic patterns, lakehouse architectures, and data mesh. Enablement, Intellectual Property, and Field Feedback Build reusable IP through reference architectures, accelerators, frameworks, and technical best practices that make future engagements faster and more repeatable. Mentor Solution Architects and solutions engineers, guiding technical development and growing bench strength across partner platforms and AI solution patterns. Feed structured field insights back to Platform Engineering, Product, and partner alli