Value Engineer

deepset

3d•Remote

About The Position

We're hiring a Value Engineer to guide public sector customers from first conversation to long-term success. You'll help Sales to qualify and win the right deals, design solutions and services engagements that work in the real world. The objective is to ensure customers reach value quickly, own the coordination of all resources required for value realization, and proactively create opportunities to grow the relationship. This role is a prescriptive, opinionated authority on which AI use cases make both business and technical sense. You'll develop a structured account thesis from day one and tie every decision back to measurable outcomes. At deepset, we're on a mission to make custom AI solutions accessible to every organization. With Haystack, thousands of developers build advanced LLM applications every day, while our enterprise-ready AI Platform helps companies turn large language models into business value. We're remote-first, flexible, and built on a culture of trust and ownership. You'll collaborate with top-tier tech talent, tackle meaningful challenges, and help transform complex AI into solutions that are simple, powerful, and ready for the real world.

Requirements

Software engineering background with significant experience in Sales Engineering, Solution Consulting, or Technical Implementation.
Autonomous commercial capability and the ability to craft and deliver tailored value messaging, position deepset's offering against alternatives, and operate as a revenue-contributing partner to Sales without requiring hand-holding.
Competitive positioning instinct and the ability to articulate deepset's differentiation clearly, handle objections, and use customer evidence and peer benchmarks as commercial tools.
Practical Python skills for scripting, prototyping, and troubleshooting.
Experience with AI/ML workflows, such as fine-tuning, data preparation, model evaluation, and RAG pipeline design.
Strong understanding of modern architectures: APIs, integrations, IAM/security; bonus for Kubernetes, Terraform, SSO, and VPC.
Ability to get hands-on with data, SQL, and light integrations.
Strong orchestration and project leadership skills: ability to coordinate cross-functional teams (TPMs, FDEs, Partners) effectively, while maintaining ownership of value outcomes.
Executive-level communication, with strong structured storytelling for C-suite and LoB audiences, not just technical audiences.
Commercial awareness: services scoping, ROI framing, and a focus on measurable outcomes.

Nice To Haves

Demonstrates strong systems thinking paired with tactical execution, with sound judgment to choose the right approach at the right time.
Experience engaging with or selling into public sector organizations, with familiarity with procurement cycles and compliance constraints.
Prior experience working with or alongside a partner ecosystem to extend delivery capability.

Responsibilities

Lead structured, multi-persona discovery to uncover goals, challenges, KPIs, and decision-making dynamics across all relevant stakeholders; from end users and Line of Business owners to technical architects and executive sponsors.
Identify the full landscape of stakeholder needs; distinguishing between technical requirements, business outcomes, and organizational constraints; being able to adjust pitch, messaging, and solution positioning autonomously per persona.
Act as a sparring partner with Enterprise Sales on deal quality, qualification rigor, and expansion potential, with a shared accountability for revenue closure.
Translate customer needs into clear technical and business solutions, and define the full offering required for the customer to succeed; including scoped services engagements covering effort, team and project structure (including partner involvement if required).
Build demos and POCs that prove value against defined business metrics, not just technical feasibility.
Own AI risk management across project feasibility, data evaluation/readiness and solution adoption.
Act as the orchestration layer above the project lifecycle and collaborate with our Technical Project Managers and Solution Engineers to Define the rollout structure, success conditions, and resource accountability; ensuring the delivery team executes against them.
Drive the plan to "first value" and own the broader rollout plan beyond it, including phased milestones, go-live readiness criteria, and post-launch stabilization.
Coordinate customer and internal teams (FDEs, CS, Partners) so delivery stays aligned to the value commitments made in pre-sales.
Monitor adoption KPIs and strategic success metrics defined at the outset, and surface deviations early.
Proactively identify blockers across the full success ecosystem: missing integrations, end-user experience gaps, training deficits, organizational change barriers, and partner capability needs; and take ownership of resolving or escalating them.
Ensure documentation, runbooks, and training plans are in place so customers can operate independently and achieve sustained adoption.
Track usage, performance, and business outcomes against the success criteria defined in the account thesis.
Run structured check-ins and QBRs that actively articulate the value delivered and reinforce deepset's differentiation; not just status updates.
Continuously narrate value in a way that maintains executive alignment and competitive positioning.
Resolve escalations and ensure systemic issues are addressed, not just patched.
Build and maintain a use case pipeline for each account: identify the next two to three use cases beyond the current scope, prioritize by feasibility and business impact, and develop a plan to advance them.
Drive urgency with the customer through executive engagement, milestone anchoring, and proactive surfacing of opportunity cost of inaction.
Run structured product update cadences to keep customers informed of roadmap progress, new capabilities, and relevant beta opportunities; positioning deepset's evolution as a competitive advantage.
Use peer benchmarking and cross-customer case studies to inspire expansion, validate investment, and reinforce the customer's confidence in their AI strategy.
Partner with Sales to build expansion value cases and support forecasting, with a clear handoff: the VE creates and qualifies the opportunity; Sales owns the close.
Bring structured, signal-rich feedback to Product and Engineering that reflects real deployment experience, not just feature requests.
Balance breadth of customer signal with prioritization judgment, distinguishing strategic product gaps from one-off edge cases.