Rackspace Technology

Forward Deployed Engineer

Rackspace Technology

full-time

Posted on:

Location Type: Remote

Location: India

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Tech Stack

CloudDockerERPETLGoJavaScriptKubernetesNode.jsNoSQLPythonReactSQLVue.js

About the role

  • 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
  • Health insurance
  • 401(k) matching
  • Flexible working hours
  • Paid time off
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

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