Vp, Customer Engineering & Value Engineering

Armada Armada · Enterprise · United States · Remote · S&M - Sales

VP of Customer Engineering & Value Engineering to lead a global team focused on AI infrastructure and edge computing. The role involves guiding customers through the pre-sales technical lifecycle, from discovery to validated, deployment-ready architectures and quantified business outcomes. This includes scoping requirements, framing AI infrastructure trade-offs, validating feasibility, and ensuring qualification before deep engineering engagement. The role emphasizes building and scaling the CE and VE functions, driving AI-focused technical discovery, and elevating global pre-sales excellence.

What you'd actually do

  1. Lead, coach, and develop a globally distributed team across North America, EMEA, APAC, and emerging markets
  2. Own global hiring strategy across Customer Engineering and Value Engineering, building presence in new regions and scaling existing teams
  3. Establish operating rhythms, onboarding frameworks, and standards for technical and commercial excellence worldwide
  4. Champion a rigorous AI-first discovery methodology, integrating both technical and value-based frameworks
  5. Set the global bar for: Architecture quality, Discovery rigor, Value articulation, Proof-of-value frameworks

Skills

Required

  • Leadership and team management
  • Customer engagement and pre-sales
  • AI infrastructure and edge computing knowledge
  • Solution architecture
  • Value engineering and ROI articulation
  • Global operations management
  • Hiring and team scaling

Nice to have

  • Experience in regulated environments (defense, energy)
  • Deep understanding of AI/ML pipelines and inference

What the JD emphasized

  • AI infrastructure
  • edge computing
  • GPU-accelerated inference
  • real-time edge AI
  • AI-powered edge platform
  • mission-critical AI ambitions
  • complex operational environments
  • AI infrastructure trade-offs
  • AI workload requirements
  • Data sovereignty and regulatory constraints
  • Connectivity realities
  • AI inference, real-time analytics, and ML pipelines
  • Value quantification, ROI modeling, and executive-level narratives
  • Architecture validation
  • Business case validation

Other signals

  • AI infrastructure
  • edge computing
  • GPU-accelerated inference
  • real-time edge AI
  • AI-powered edge platform