Solutions Engineer (US)

Summary

Qdrant is an open-source vector search engine powering AI applications such as semantic search, retrieval-augmented generation, AI agents, and real-time recommendations. The Solutions Engineer will act as a technical guide for clients, supporting deal execution, architectural reviews, migration assessments, deployment risk identification, and resilient search architecture design.

Responsibilities

• 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

Skills

• 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
• Experience with vector databases or search technologies (Elasticsearch, Solr, OpenSearch, Lucene, or similar)
• Exposure to enterprise procurement processes, infosec questionnaires, or MSA redlines

Qualifications

Must Haves

• 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 Haves

• Experience with vector databases or search technologies (Elasticsearch, Solr, OpenSearch, Lucene, or similar)
• Exposure to enterprise procurement processes, infosec questionnaires, or MSA redlines

Benefits

• A remote-first, international team working on cutting-edge AI infrastructure.
• 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, comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy.