AI Value Engineer
At a glance
Mid-level Solutions & Sales Engineering role at DDN. Madrid · full-time.
Pay not stated
Growth Roles summary, based on the employer's posting.
What you'll do
- Lead AI value workshops that connect customer strategies, workloads, and infrastructure issues to measurable outcomes
- Build ROI, TCO, and investment cases showing the financial effects of AI infrastructure choices
- Create reusable AI value methods, tools, and frameworks for different customers, industries, and sales cycles
- Develop AI value messaging and proof points with Product Marketing and Product teams
- Support major enterprise opportunities while tracking customer outcomes, pipeline movement, and revenue impact
What you bring
- At least five years in value engineering, consulting, strategy, solutions consulting, sales engineering, or a comparable customer-facing role
- Hands-on experience creating ROI analyses, TCO assessments, financial models, and investment cases
- A bachelor’s degree in business, economics, engineering, computer science, or a related field
- Strong analysis and executive communication skills, including the ability to explain complex information commercially
- Comfort managing lengthy enterprise sales processes across several stakeholders and working across countries and cultures
Who this fits
You’ll suit a customer-facing role that combines AI infrastructure, commercial analysis, executive presentations, and enterprise sales support. The position is based in Madrid with a hybrid setup and covers EMEA. Fluent English is required, while another European language is a plus.
From the employer
AI Value Engineer, EMEA
About the Role
The AI Value Engineer, EMEA, will help enterprise customers quantify and communicate the business value of DDN’s AI and data infrastructure solutions.
As part of DDN’s global Go to Market Centre of Excellence, you will support strategic customer opportunities while developing reusable value frameworks, tools, and methodologies for Sales and Solution Engineering teams worldwide.
You will operate at the intersection of AI, technology, strategy, and commercial value—translating complex infrastructure decisions into compelling demonstrations, measurable outcomes, and clear investment cases for both technical and C-level audiences.
Key Focus Areas
Demo Excellence: Enhancing how DDN presents its technology and customer value during sales engagements.
Scalable Value Engineering: Creating repeatable frameworks and tools for value discovery, ROI, TCO, and business case development.
Field Enablement: Helping customer-facing teams and partners use value engineering assets confidently and effectively.
Commercial Impact: Supporting strategic opportunities and measuring how CoE initiatives contribute to customer outcomes, pipeline progression, and revenue.
Responsibilities
Lead AI value discovery workshops with enterprise customers and prospects.
Understand customer AI strategies, use cases, workloads, and infrastructure challenges, translating them into measurable business outcomes.
Build compelling ROI, TCO, and business case models for AI and data infrastructure investments.
Quantify the financial impact of AI infrastructure decisions, including improvements in productivity, utilization, performance, scalability, operational efficiency, and time-to-value.
Develop AI-specific value frameworks, methodologies, and tools that can be reused across customers, industries, and sales cycles.
Help customers understand the economics of AI, from experimentation and proof-of-concept through to production-scale deployment.
Partner with Product Marketing and Product teams to develop AI value messaging, customer proof points, and success metrics.
Build and maintain benchmarks, market intelligence, and competitive insights relating to AI infrastructure and economics.
Help establish and scale DDN’s AI Value Engineering Centre of Excellence.
Qualifications
5+ years of experience in Value Engineering, Management Consulting, Strategy, Solutions Consulting, Sales Engineering, or a similar customer-facing role.
Experience building ROI, TCO, financial models, and investment business cases.
Strong analytical skills, with the ability to turn complex data into a clear commercial story.
Excellent presentation and communication skills, particularly with senior executives.
Comfortable working across long, complex enterprise sales cycles and multiple stakeholders.
Ability to work across multiple countries and cultures in a fast-moving environment.
Bachelor’s degree in Business, Economics, Engineering, Computer Science, or a related discipline.
Preferred Qualifications
Understanding of AI/ML workloads and the infrastructure required to support them.
Experience working with enterprise customers on AI strategy, transformation, or technology investment decisions.
Fluent English; additional European language skills are a plus.
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