Full-Time
Value Engineer
deepset
51-200 employees
Provides NLP AI tooling for enterprises
No salary listed
Barcelona, Spain + 1 more
More locations: Berlin, Germany
Remote
 Consulting (1) |
|---|
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- A software engineering background with significant experience in Sales Engineering, Solution Consulting, or Technical Implementation.
- Autonomous commercial capability, including crafting and delivering tailored value messaging, positioning the offering against alternatives, and operating as a revenue-contributing partner to Sales.
- The ability to articulate product differentiation clearly, handle objections, and use customer evidence and peer benchmarks as commercial tools.
- Practical Python skills for scripting, prototyping, and troubleshooting.
- Experience with artificial intelligence and machine learning workflows, including fine-tuning, data preparation, model evaluation, and retrieval-augmented generation pipeline design.
- A strong understanding of modern architectures, including application programming interfaces, integrations, identity and access management, and security; Kubernetes, Terraform, single sign-on, and virtual private cloud experience is a bonus.
- The ability to work hands-on with data, SQL, and light integrations.
- Strong orchestration and project leadership skills, including coordinating cross-functional teams effectively while maintaining ownership of value outcomes.
- Executive-level communication and structured storytelling for C-suite and line-of-business audiences.
- Commercial awareness, including services scoping, return-on-investment framing, and focus on measurable outcomes.
- Lead structured, multi-persona discovery to uncover goals, challenges, key performance indicators, and decision-making dynamics across relevant stakeholders.
- Identify stakeholder needs and distinguish technical requirements, business outcomes, and organizational constraints while adjusting pitch, messaging, and solution positioning per persona.
- Act as a sparring partner with Enterprise Sales on deal quality, qualification rigor, and expansion potential, with shared accountability for revenue closure.
- Translate customer needs into clear technical and business solutions and define the complete offering required for customer success, including scoped services engagements, effort, team and project structure, and partner involvement when required.
- Build demonstrations and proofs of concept that prove value against defined business metrics.
- Own artificial intelligence risk management across project feasibility, data evaluation and readiness, and solution adoption.
- Define rollout structure, success conditions, and resource accountability with Technical Project Managers and Solution Engineers, ensuring delivery teams execute against them.
- Drive the plan to first value and own the broader rollout plan, including phased milestones, go-live readiness criteria, and post-launch stabilization.
- Coordinate customer and internal teams, including FDEs, Customer Success, and partners, so delivery remains aligned with pre-sales value commitments.
- Monitor adoption key performance indicators and strategic success metrics and surface deviations early.
- Identify blockers across integrations, end-user experience, training, organizational change, and partner capability, then resolve or escalate 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 success criteria defined in the account thesis.
- Run structured check-ins and quarterly business reviews that articulate delivered value and reinforce differentiation.
- Maintain executive alignment and competitive positioning by continuously communicating value.
- Resolve escalations and ensure systemic issues are addressed.
- Build and maintain a use-case pipeline for each account, prioritize future use cases by feasibility and business impact, and develop plans to advance them.
- Drive customer urgency through executive engagement, milestone anchoring, and proactive communication of the opportunity cost of inaction.
- Run structured product-update cadences covering roadmap progress, new capabilities, and relevant beta opportunities.
- Use peer benchmarking and cross-customer case studies to inspire expansion and validate investment.
- Partner with Sales to build expansion value cases and support forecasting, with the Value Engineer creating and qualifying opportunities and Sales owning the close.
- Bring structured, signal-rich customer feedback to Product and Engineering based on real deployment experience.
- Distinguish strategic product gaps from one-off edge cases when prioritizing customer feedback.
- Experience engaging with or selling into public sector organizations, including familiarity with procurement cycles and compliance constraints.
- Prior experience working with or alongside a partner ecosystem to extend delivery capability.
- Strong systems thinking paired with tactical execution and sound judgment in choosing the right approach at the right time.
Deepset builds developer tools to help create production-ready AI and NLP systems. Its Haystack open-source framework lets engineers design pipelines for tasks like semantic search, document processing, and retrieval-augmented generation, and it works with multiple models and providers. On top of Haystack, Deepset offers Haystack Enterprise Platform as a SaaS for prototyping, deployment, monitoring, and governance, with options for cloud, on-premise, or air-gapped setups. The company aims to help organizations build scalable AI-powered applications that can search and reason over large data sets.
Company Size
51-200
Company Stage
Early VC
Total Funding
$45.6M
Headquarters
Berlin, Germany
Founded
2018
Get referred to deepset
See people who can refer or advise you
Simplify's Take
What believers are saying
- deepset launched Haystack Enterprise Platform on August 2, 2026, expanding governed deployments.
- HPE Unleash AI and Celonis partnerships opened defense, public-sector, and critical-infrastructure channels.
- AI@EC and NTT DATA deployments validate regulated demand across Europe.
What critics are saying
- Celonis controls the operational context layer, limiting deepset’s leverage in joint deals.
- Hyperscalers like OpenAI, Microsoft, and AWS bundle orchestration, crushing standalone platform pricing.
- If sovereign AI contracts stall, deepset’s platform becomes a feature inside larger vendors.
What makes deepset unique
- Haystack powers deepset’s open-source, vendor-neutral agent orchestration across models and infrastructure.
- European Commission AI@EC runs on deepset, proving sovereign deployments in production.
- Celonis and HPE partnerships position deepset as Europe’s sovereign AI layer.
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Benefits
Remote Work Options
Flexible Work Hours
Paid Vacation
Paid Sick Leave
Stock Options
Professional Development Budget
Mental Health Support
Company Social Events
Growth & Insights and Company News
6 month growth
↑ 2%1 year growth
↓ -2%2 year growth
↑ 6%deepset and HPE team up to boost sovereign AI deployments. The marriage of governance and AI. From Silicon Valley to sovereign shores, AI is making waves, but the path ain't always smooth. Today, deepset and HPE are shaking things up a notch, building a robust bridge between AI's potential and the stringent demands of sovereign environments. If you're snoozing on this, you're missing out on the seismic shift in AI deployment strategies. AI deployment: the new battleground. The partnership isn't just another techie pow-wow - it's a tactical move in the evolving game of AI governance. With the Haystack Enterprise Platform backing deepset's play, organizations can now blitz through AI use case rollouts while keeping risk, regulations, and rights in check. AI and governance coming together like this? It's the future, folks. Investors Hangout, LLC is talking about deploying AI systems that get self-hosted safely within sovereign stickers and air-gapped barricades. All while keeping those sensitive data sets and models securely under lock and key. "With agentic AI moving into operational deployment, organizations need infrastructure and AI platforms they can control and trust," Milos Rusic, deepset's CEO, emphasized the need for sovereignty in today's AI stageplay. Unpacking the HPE connection. Let's not forget HPE's role in this whole dance. The HPE Unleash AI partner program, all shimmer and shine, paints a picture of engineered AI success that is as flexibly governed as it is robust. Yeah, Investors Hangout, LLC is talking about stacking up HPE Private Cloud AI with NVIDIA's wizardry for a system ready to nun-chuck its way into production AI. Why sovereign AI matters. In a world that can't seem to stop talking data breaches and cyber threats, having control over AI deployment is more important than ever. And if you're the betting type, wagering on deepset and HPE's capabilities to bolster that control might just see your portfolio climb. They're arming enterprises with the tools to enforce governance, auditability, and lifecycle management. This isn't just for the tech geeks or defense wonks. Sovereign AI extends its appeal to organizations ranging from the public sector to the boardroom table. Whether it's using AI for cybersecurity in classified operations or supporting mission-critical decision-making, this collaboration covers the gamut. There's More in the AI Future. deepset's pedigree isn't something to overlook. With prior collaborations involving heavyweights like the European Commission and the German Ministry of Research, Technology, and Space, they're no strangers to tailoring AI operations under strict governance. In essence, by navigating the rough AI waters hand in hand with HPE, deepset's setting a precedent. They're not only prepping enterprises for the present but future-proofing them for what's bound to be a tech-dominated era. "Together, HPE and deepset are delivering secure, governed AI solutions that combine enterprise-grade infrastructure with flexible and governed AI agent capabilities," chimed in Robin Braun of HPE, spelling out the essence of their collaboration. Closing thoughts: from aspiration to action. With the world getting tighter around data control, the deepset-HPE collaboration is a powerful move. But let's be clear - this partnership isn't an overnight fix. It's an evolving strategy where patience meets preparedness, and that blend might just be what's needed in today's high-stakes AI landscape. If you're running an enterprise, it's time to strap in and watch as these AI forces give the game a fresh spin.
Cooperation is Europe's answer to Palantir. Celonis and Deepset are joining forces for a sovereign AI platform for government agencies and critical infrastructures. The goal is a market that has so far been dominated by the controversial US corporation. 16.06.2026 - 4:11 pm Celonis co-CEO Bastian Nominacher (center) in conversation at the digital conference TECH on June 1, 2026 in Heilbronn Photo: Marc-Steffen Unger
Celonis and deepset launch sovereign AI platform for critical operations. News | June 16, 2026 By Asma Adhimi German software companies Celonis and deepset have teamed up to develop a sovereign AI platform aimed at mission-critical environments, including public sector organizations, defense, cybersecurity, policing, and critical infrastructure. The partnership combines process intelligence with AI agent orchestration to help organizations deploy AI systems while maintaining control over data, models, and infrastructure. For eeNews Europe readers, the announcement highlights the growing push for European-controlled AI technologies at a time when governments and critical infrastructure operators are seeking alternatives to non-European platforms. It also reflects increasing demand for AI systems that can operate in highly regulated and security-sensitive environments. European alternative for sovereign AI. The partnership brings together Celonis' Process Intelligence Platform and its recently introduced Celonis Context Model (CCM) with deepset's Haystack-based AI agent platform. The companies say the integrated platform is designed to provide organizations with a trusted AI environment that can unify operational data, documents, and workflows. The goal is to improve situational awareness, accelerate investigations, identify operational risks earlier, and support decision-making across departments and agencies. A key feature is deployment flexibility. According to the companies, the platform can run on an organization's preferred infrastructure without creating vendor lock-in, allowing users to retain full control over their data and AI operations. "At deepset, we believe organizations need AI systems they can fully trust and control - especially in mission-critical environments," said Milos Rusic, CEO and co-founder of deepset. "By combining Haystack's open and governed AI agents with the Celonis Context Model, we are creating a European alternative for operational AI that is transparent, auditable, and grounded in real-world operational context." Process intelligence meets AI orchestration. The collaboration addresses one of the major challenges facing enterprise AI deployments: providing systems with accurate operational context. Celonis says its Process Intelligence Platform and CCM allow AI systems to understand how organizations function in real time by connecting process data, business knowledge, and decision intelligence. The company argues that this operational layer is often missing from enterprise AI implementations. "AI systems are only as effective as the operational context they are given," said Florian Schewior, Managing Director DACH at Celonis. "By combining Celonis Process Intelligence and Context Model capabilities with deepset's AI orchestration platform, we can help public sector and security organizations deploy AI systems that are not only powerful, but operationally grounded, explainable, and aligned with sovereign infrastructure requirements." Focus on Europe's security and public sector markets. The initial focus of the partnership will be on organizations in the DACH region, while also supporting broader European sovereign AI initiatives linked to security, defense, and public sector modernization programs. The companies said they plan to expand their collaboration through joint customer projects, ecosystem partnerships, and industry events as demand for sovereign AI solutions continues to grow across Europe. Celonis, headquartered in Munich and New York, has built its reputation around process intelligence software, while Berlin-based deepset develops the open-source Haystack framework for building AI applications and agents. Together, they are positioning the new platform as a European-built alternative for organizations that require both advanced AI capabilities and strict control over operational data. Linked Articles
AI Sovereignty: what Breaks When You Build AI. Bilge Yücel from deepset GmbH explains the engineering challenges and solutions for building sovereign AI systems, focusing on data, model, infrastructure, and operational control. In the rapidly evolving AI landscape, the concept of sovereignty has emerged as a critical factor, particularly for organizations operating in regulated sectors or those prioritizing data privacy and control. Bilge Yücel, Sr. DevRel at deepset GmbH, delivered a presentation titled "What Breaks When You Build AI Under Sovereignty Constraints" at AI Engineer Europe, shedding light on the complexities and considerations involved in developing Sovereign AI systems. Visual TL;DR. AI Sovereignty Need leads to Engineering Challenges. Engineering Challenges addressed by Four Pillars. Four Pillars leads to Data Sovereignty. Four Pillars leads to Model Sovereignty. Four Pillars leads to Infrastructure Sovereignty. Four Pillars leads to Operational Sovereignty. Engineering Challenges with Solutions Provided. Solutions Provided using Haystack Integration. Haystack Integration enables Sovereign AI Systems. * AI Sovereignty Need: organizations need control over data, models, infrastructure, operations * Four Pillars: data, model, infrastructure, and operational control are key * Data Sovereignty: governs how data is accessed and used in AI systems * Model Sovereignty: control over the choice and development of AI models * Infrastructure Sovereignty: control over the underlying hardware and software stack * Operational Sovereignty: control over the deployment and ongoing management of AI * Engineering Challenges: complexities in building AI under sovereignty constraints * Solutions Provided: addressing challenges with specific engineering approaches * Haystack Integration: bringing it all together with deepset's Haystack framework * Sovereign AI Systems: design, deploy, and operate AI on own terms Visual TL;DR Understanding Sovereign AI. Yücel defined Sovereign AI as the ability of an organization to design, deploy, and operate AI systems on its own terms. This entails having explicit control over data flow, model choice, infrastructure, and operations. She broke down the concept into four key pillars: Data Sovereignty, Model Sovereignty, Infrastructure Sovereignty, and Operational Sovereignty. The Four Pillars of Sovereign AI. Data Sovereignty governs how data is accessed and used in AI systems, emphasizing that data should be stored and processed within trusted jurisdictions to meet compliance requirements, and that access permissions must be respected. Yücel highlighted that for European citizens, data sovereignty often means data must remain within Europe, citing GDPR as a prime example.
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