Forward Deployed Engineer II
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Job Description
Forward Deployed Engineer at Rackspace Technology working with enterprise customers to create AI solutions. Leading implementation, design, and customer engagement throughout project lifecycles.
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.
Benefits:
- Professional development opportunities
- Flexible working hours
- Health insurance
- Paid time off
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