Sr. Forward Deployed Engineer - Private Cloud, Data & AI Enterprise Solutions

Rackspace Technology is a global technology company, and they are seeking a Sr. Forward Deployed Engineer to work with strategic enterprise customers on high-impact AI solutions. The role involves architecting, building, and deploying AI applications while serving as the technical bridge between the company's capabilities and customer challenges.

Responsibilities:

• Embed with strategic enterprise customers to rapidly 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 (LlamaIndex, Haystack) and orchestrate multi-agent workflows (LangChain, LangGraph, CrewAI)
• Ship with full-stack and DevOps 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

Requirements:

• BS/MS/PhD in Computer Science, Data Science, Engineering, Mathematics, Physics, or related field
• 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/NoSQL databases
• Hands-on with LLMs, prompt engineering, vector databases, data pipelines, application dashboards, RAG pipelines, and agent orchestration frameworks
• Strong DevOps 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 tight timelines
• Excellent communication skills - comfortable with C-suite presentations, technical workshops, and cross-functional collaboration
• Willingness to travel up to 25% for on-site customer engagements
• Experience with Palantir Foundry, AIP, ontology modeling, Uniphore BAIC, or similar Enterprise AI development platforms
• Knowledge of SLM fine-tuning, model distillation, RLHF, and AI evaluation frameworks
• Experience building agentic AI solutions: multi-agent systems, tool use, and autonomous workflow orchestration
• Familiarity with GPU infrastructure (NVIDIA H100/B200, InfiniBand) and private cloud platforms (OpenStack, VMware)
• Foundry certifications from Palantir/Uniphore or AI/ML-related certifications
• Prior experience in technology consulting, AI startups, or Forward Deployed / Solutions Engineering roles
• Domain expertise in financial services, healthcare, supply chain, defense, energy, or manufacturing
• Experience with knowledge graphs, semantic modeling, and ontology-driven data management