Palantir Deployment Engineer - Principal level

AMroute LLC; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

Rackspace Technology is seeking a Forward Deployed Engineer to embed with strategic enterprise customers, architecting, building, and deploying high-impact AI solutions. You will own the full solution lifecycle from discovery to production deployment, serving as the technical bridge between Rackspace AI capabilities and customer challenges.

This role demands strong software/data engineering and customer-facing leadership, with hands-on work across LLMs, RAG pipelines, and enterprise data

Qualifications

• Must be Palantir certified.
• 6+ years in software engineering, data engineering, or AI/ML delivery; 4+ years customer-facing or field roles.
• Proven track record in building and deploying AI/ML applications at enterprise scale.
• Hands-on with LLMs, prompt engineering, vector databases, data pipelines, dashboards, RAG pipelines, and agent orchestration frameworks.
• Strong DevOps skills: Docker, Kubernetes, CI/CD, GPU infrastructure, cloud-native deployment patterns.
• Experience integrating across ERP, data warehouses, data lakes, streaming architectures.
• Translate ambiguous customer needs into actionable engineering plans under tight timelines.
• Excellent communication skills - comfortable with C-suite presentations, workshops, and cross-functional collaboration.
• Experience with Palantir Foundry, AIP, ontology modeling, Uniphore BAIC, or similar platforms.
• Knowledge of SLM finetuning, distillation, RLHF, and AI evaluation frameworks.
• Experience building agentic AI solutions: multi-agent systems, tool use, autonomous workflow orchestration.
• Familiarity with GPU infrastructure (NVIDIA H100/B200, InfiniBand) and private clouds (OpenStack, VMware).
• Prior experience in technology consulting, AI startups, or Forward Deployed roles.
• Domain expertise in financial services, healthcare, supply chain, defense, energy, or manufacturing.
• Experience with knowledge graphs, semantic modeling, and ontology-driven data management.

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 AI agents in production.
• Identify expansion opportunities by working with sales and customer success to uncover high-value use cases.
• Feed structured field insights back to Platform Engineering and Product on feature gaps and usability improvements.
• Build reusable IP through reference architectures, accelerators, frameworks, and best practices for future engagements.
• Mentor engineers and customer teams, driving knowledge transfer and building internal AI competencies.

Skills

Palantir Foundry

LLMs

Prompt engineering

Data pipelines

Agent orchestration

Knowledge graphs

Ontology modeling

ERP integration

Data lakes/warehouses

LangChain

Multi-agent systems

Tools

Docker

Kubernetes

CI/CD

GPU infrastructure

OpenStack

VMware

Pinecone

Weaviate

AstraDB

Job description

Location: Onsite: Remote - 25% Travel (Preferably in Texas but not a requirement

Note: Senior or principal level resources, they must have Palantir foundry experience and certifications.

Job Summary: As a 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.

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.
• 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.

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.