Forward Deployed Engineer

Rackspace Technology

Forward Deployed Engineer at Rackspace Technology architecting AI solutions for enterprise customers. Owning the full solution lifecycle and collaborating with strategic enterprise clients.

Posted 4/16/2026full-timeRemote • 🇮🇳 IndiaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

CloudDockerERPETLGoJavaScriptKubernetesNode.jsNoSQLPythonReactSQLVue.js

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

Health insurance
401(k) matching
Flexible working hours
Paid time off

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonNode.jsGoReactVueSQLNoSQLETLELTLLM

Soft Skills

communicationmentoringcollaborationproblem-solvingcustomer engagementknowledge transferleadershipadaptabilitytime managementpresentation

Certifications

BS in Computer ScienceMS in Data SciencePhD in EngineeringPhD in MathematicsPhD in Physics