Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations.
Trusted by global leaders like Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom, we’re building the retrieval infrastructure layer for modern AI. Recently raising $50M in Series B funding, we are growing rapidly and committed to transforming how AI understands and interacts with data.
As a remote-first company, we believe diverse backgrounds, perspectives, and experiences fuel innovation. Here, you’ll own meaningful work, tackle challenges, and grow alongside passionate individuals dedicated to shaping the future of AI.
We're looking for a Solutions Engineer to own the technical side of our enterprise deals, partnering with Account Executives as the technical authority rather than a supporting player. You'll lead the scoping, pricing, and deployment of semantic search, image and video search, and on-premise and edge deployment solutions for our most complex enterprise customers. This is a hands-on, customer-facing role for someone with real depth in solutions engineering who's ready to run high-stakes deals independently.
What you will own:
- Own Technical-to-Commercial Strategy: Translate complex architectural requirements (multi-AZ, sharding, replication) into accurate pricing models and contract terms, independently protecting deal value in high-stakes negotiations.
- Drive Deal Execution End to End: Own the technical sales workflow across Support, Product, and Engineering to unblock POCs, resolving cross-functional bottlenecks without escalation.
- Identify and Mitigate Deployment Risk: Proactively spot noisy neighbor issues, latency bottlenecks, and resource contention before they threaten renewals or expansions, and advise customers on architectural fixes.
- Lead Architectural Reviews and Migration Assessments: Run discovery sessions solo, validate use cases, and design migration paths from legacy search engines like Elastic, Solr, and OpenSearch to Qdrant.
- Design Resilient, Production-Grade Architecture: Spec high-availability search clusters that meet demanding customer SLAs and throughput requirements, including for the most technically complex accounts.
- Own Client Trust at the Executive Level: Serve as the primary technical authority during evaluation, guiding customers to value quickly and acting as a long-term advisor beyond the initial sale.
Who you are
- 2-5 years of technical, customer-facing experience in Sales Engineering, Solutions Architecture, or Technical Consulting, with a track record of owning complex, high-value deals independently, including at least 2 years of direct, hands-on vector search experience (Qdrant, Pinecone, Weaviate, Zilliz/Milvus, or similar). Background in data science, computer engineering, or a previous DevOps/SRE role, with demonstrated depth rather than surface exposure is preferred.
- Strong scripting ability in Python or Bash, used to build reusable tools and validate business value at scale, not just one-off scripts.
- Fluency in calculating total cost of ownership (TCO) and estimating hardware requirements (RAM, CPU, disk) for distributed systems, including for multi-region or highly regulated deployments.
- Deep expertise in Kubernetes and cloud platforms (AWS, GCP, or Azure), with a clear point of view on how infrastructure choices affect cost and performance at scale.
- Excellent communication skills, comfortable running the room with engineers, VPs, and business stakeholders alike, and able to influence without authority across internal teams.
Preferred Qualifications
- Exposure to enterprise procurement processes, infosec questionnaires, or MSA redlines.
WHY JOIN US
- A remote-first, international team working on cutting-edge AI infrastructure.
- A competitive salary with additional perks.
- Flexible working hours and async-friendly culture.
- High ownership and real impact.
- Open-source, engineering-driven culture.
- Choose your own laptop equipment.
For US-based full-time employees, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy.
Qdrant is an equal-opportunity employer. We believe the best ideas come from diverse teams, and we actively welcome applicants from all backgrounds. If this role excites you but you don't check every single box, we'd still love to hear from you! We don't want to miss out on great people because of a checklist.
Come build with us!
FOR INFORMATION ON HOW WE HANDLE YOUR PERSONAL DATA, PLEASE REFER TO OUR RECRUITMENT PRIVACY POLICY qdrant.tech
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Requirements
Must-haves
Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations.
Drive Deal Execution End to End: Own the technical sales workflow across Support, Product, and Engineering to unblock POCs, resolving cross-functional bottlenecks without escalation.
Identify and Mitigate Deployment Risk: Proactively spot noisy neighbor issues, latency bottlenecks, and resource contention before they threaten renewals or expansions, and advise customers on architectural fixes.
Lead Architectural Reviews and Migration Assessments: Run discovery sessions solo, validate use cases, and design migration paths from legacy search engines like Elastic, Solr, and OpenSearch to Qdrant.
Design Resilient, Production-Grade Architecture: Spec high-availability search clusters that meet demanding customer SLAs and throughput requirements, including for the most technically complex accounts.
Own Client Trust at the Executive Level: Serve as the primary technical authority during evaluation, guiding customers to value quickly and acting as a long-term advisor beyond the initial sale.
Strong scripting ability in Python or Bash, used to build reusable tools and validate business value at scale, not just one-off scripts.
Fluency in calculating total cost of ownership (TCO) and estimating hardware requirements (RAM, CPU, disk) for distributed systems, including for multi-region or highly regulated deployments.
Deep expertise in Kubernetes and cloud platforms (AWS, GCP, or Azure), with a clear point of view on how infrastructure choices affect cost and performance at scale.
Excellent communication skills, comfortable running the room with engineers, VPs, and business stakeholders alike, and able to influence without authority across internal teams.
Nice-to-haves
Exposure to enterprise procurement processes, infosec questionnaires, or MSA redlines.
Application prep
Use the role’s language and application details while tailoring your materials.