Forward Deployed Engineer II - IN

Rackspace Technology is a multicloud solutions expert that combines its expertise with leading technologies to deliver end-to-end solutions. The Forward Deployed Engineer II will work directly with enterprise customers to architect and deploy AI solutions, owning the full solution lifecycle from discovery to deployment and optimization.

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
• 3+ years in software engineering, data engineering, or AI/ML delivery; 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