Solutions Engineer at Qdrant
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About Qdrant
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.
Role Overview
As a Solutions Engineer, you'll take ownership from day one, acting as a technical guide for our clients. We're looking for someone with a strong builder mindset who's self-reliant and truly passionate about vector search. An entrepreneurial streak will set you up for success in this role.
What You Will Own
Support the Technical-to-Commercial Translation: Help translate architectural requirements (Multi-AZ, sharding, replication) into accurate pricing models and contract terms, working alongside senior team members to protect deal value.
Help Drive Deal Execution: Coordinate with Support, Product, and Engineering to unblock proofs of concept (POCs), and keep the technical sales workflow moving in Jira and Salesforce.
Flag Deployment Risks Early: Learn to spot noisy neighbour issues, latency bottlenecks, and resource contention before they affect renewals or expansions.
Support Architectural Reviews and Migration Assessments: Join discovery sessions to help validate use cases and map migration paths from legacy search engines like Elastic, Solr, and OpenSearch to Qdrant.
Contribute to Resilient Architecture Design: Help spec high-availability search clusters that meet customer SLAs and throughput needs.
Build Trust with Client Stakeholders: Act as a technical point of contact during the evaluation process, helping customers reach value quickly.
Who You Are
2-4 years of technical, customer-facing experience in Sales Engineering, Solutions Architecture, or Technical Consulting.
Background in data science, computer engineering, or a previous DevOps role.
Scripting ability in Python or Bash, used to validate business value rather than for its own sake.
Ability to calculate total cost of ownership (TCO) and estimate hardware requirements (RAM, CPU, disk) for distributed systems.
Experience with Kubernetes and cloud platforms (AWS, GCP, or Azure), and how infrastructure choices affect cost and performance.
Strong communication skills: comfortable explaining technical concepts to both engineers and business stakeholders.
Nice to Have
Experience with vector databases or search technologies (Elasticsearch, Solr, OpenSearch, Lucene, or similar).
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.
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Qdrant
View Company ProfileQdrant (operating under qdrant.tech, legally Qdrant Berlin GmbH) is the premier, enterprise-grade open-source vector database management system, high-performance neural search engine, and similarity matching orchestration powerhouse engineered to act as the definitive, high-velocity infrastructure layer for Retrieval-Augmented Generation (RAG), large language model (LLM) persistent memory, and multimodal AI applications. Founded in 2021 by expert distributed systems and machine learning technologists Andre Zayarni and Andrey Vasnetsov, the corporate ecosystem completely eliminates the severe performance degradation, index-rebuilding latency spikes, and structural memory erosion commonly associated with legacy relational database plug-ins and primitive K-Nearest Neighbor (KNN) wrappers by building its custom storage architecture entirely from scratch in Rust. Moving far beyond traditional keyword search methods or high-latency post-filtering mechanisms, Qdrant natively unifies expansive JSON metadata payload filtering during HNSW graph traversal, native hybrid search combining dense and sparse vectors, ColBERT-based token-level late interaction reranking, and dynamic multivector storage configurations into a single high-availability data infrastructure framework. As the primary engine trusted by global developer communities and high-scale enterprise platforms, its open-source repository commands over 29,000 GitHub stars and powers critical AI pipelines for forward-thinking organizations globally. Backed by elite international venture groups, the enterprise has secured over $87 million in institutional funding—culminating in a massive Series B capital acceleration led by Spark Capital alongside 42 Capital and AVP to scale its managed Qdrant Cloud operations and native inference engine integrations. Under the hood, its technology stack harnesses advanced vector quantization strategies (including scalar and binary quantization) to execute up to a 64x memory footprint reduction with minimal recall loss, alongside real-time data indexing capabilities that expose newly added vector points instantaneously to active queries. Headquartered in Berlin, Germany, with a globally distributed remote engineering presence, the firm operates with absolute deterministic execution correctness across multi-tenant cloud and on-premise infrastructure environments. What sets Qdrant apart is its uncompromising dedication to replacing complex, multi-stage data orchestration loops with real-time, one-stage filtered similarity traversal and deep memory optimization; by bridging the gap between high-volume programmatic data repositories and millisecond-level neural vector retrieval, the corporation remains a definitive cornerstone of modern infrastructure scaling and worldwide applied artificial intelligence transformation.
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