Job description

Job Summary : As a Senior Forward Deployed Engineer 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: Remote - Work from Home 25% travel 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 (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. Required Qualifications: Must be Palantir certified. 6 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. 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). 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. #LI-Remote #LI-AS Our compensation reflects the cost of labor across several geographic markets. The compensation range for this position ranges from $165,830.00/year in our lowest geographic market up to 243,141.80 USD/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. The compensation package may also include incentive compensation opportunities in the form of annual bonus or incentives, equity awards and an Employee Stock Purchase Plan (ESPP). Learn more about benefits at Rackspace. About Rackspace Technology Rackspace Technology is the trusted operator of the full enterprise AI stack, from governed private cloud to AI inference and agents in production. From edge to core to cloud, we operate the infrastructure, data layer, and software integration to deliver business outcomes with the predictable cost, reliability, security, and compliance that regulated and mission-critical environments demand. We support customers in more than 120 countries with AI capabilities spanning private cloud, public cloud, applications, data and cyber resilience. More on Rackspace Technology Though we’re all different, Rackers thrive through our connection to a central goal: to be a valued member of a winning team on an inspiring mission. We bring our whole selves to work every day. And we embrace the notion that unique perspectives fuel innovation and enable us to best serve our customers and communities around the globe. We welcome you to apply today and want you to know that we are committed to offering equal employment opportunity without regard to age, color, disability, gender reassignment or identity or expression, genetic information, marital or civil partner status, pregnancy or maternity status, military or veteran status, nationality, ethnic or national origin, race, religion or belief, sexual orientation, or any legally protected characteristic. If you have a disability or special need that requires accommodation, please let us know.

Requirements

Must-haves

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.
Must be Palantir certified.
6 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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonnode.jsgoreactvuedockerkubernetesci/cdmlsql

Platform: workday
Effort: long