Senior Solutions Engineer (APAC)

Posted Yesterday
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4 Locations
In-Office or Remote
Senior level
Artificial Intelligence • Machine Learning • Software • Database
The Role
Own technical strategy for enterprise APAC deals from qualification through production deployment. Design scalable vector search architectures, build production-grade proof-of-concept systems, advise customers on AI infrastructure and deployment choices, and support strategic accounts as a technical account manager. Contribute reference architectures, technical guides, and reusable frameworks while partnering with sales teams and engaging technical and executive stakeholders.
Summary Generated by Built In

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.

As we expand our presence across Asia-Pacific, we're looking for a Senior Solutions Engineer to be the technical authority in deals that determine how enterprises build AI infrastructure for the next decade, and the trusted technical owner for our most strategic accounts in the region. Most SE roles are demo cycles. This one is different.

You'll work directly with Staff Engineers and CTOs, architect real systems, and own the technical outcome of every engagement you touch, from a first prospect call through to expansion conversations with customers you already support. We care more about time-zone overlap with our regional accounts than a specific city, so this role is open to candidates based anywhere aligned with major APAC hubs like India, Singapore, Japan, or Australia.

What you will own:
  • Own the technical strategy in enterprise deals from first call to production deployment, partnering with Account Executives to qualify, architect, and close for our entire Asian-Pacific client base

  • Act as a Technical Account Manager for a small portfolio of existing enterprise customers in the region alongside active prospects, identifying new use cases, expansion opportunities, and upsell paths, and looping in Account Executives and internal resources to advocate for the customer.

  • Design vector search architectures for high-scale workloads, including multi-tenant agentic systems, hybrid search pipelines, and low-latency retrieval at billion-vector scale.

  • Build proof-of-concept systems that customers take to production (not throw away), demonstrating Qdrant's performance advantages over JVM-based or proprietary alternatives.

  • Serve as a trusted advisor on AI infrastructure decisions, helping customers navigate migration from legacy databases, avoid architectural lock-in, and deploy across cloud, on-prem, or air-gapped environments.

  • Contribute to the field engineering knowledge base: reference architectures, technical guides, and reusable POC frameworks that scale the team's impact.

Who you are
  • 5+ years of pre-sales or solutions engineering, ideally with a background in data infrastructure or systems architecture. Hands-on infrastructure depth alone isn't enough, we need a proven track record running technical qualification and proof-of-concept processes within a live sales cycle.

  • Experience designing or operating distributed systems, search infrastructure, or data pipelines at production scale.

  • Ideally, hands-on experience with RAG (retrieval-augmented generation) pipelines specifically, plus working knowledge of the broader modern AI stack (LLMs, embedding models, agentic frameworks).

  • Ideally, working knowledge of a structured sales qualification framework such as MEDDICC/MEDDPICC, and the ability to apply it in real deal conversations, connecting technical decisions to business outcomes and deal strategy.

  • Strong communicator across audiences: you can go deep on indexing trade-offs with a Staff Engineer and explain infrastructure ROI to a CIO in the same afternoon.

Nice to have
  • Experience with vector search, approximate nearest neighbor algorithms, or semantic retrieval systems.

  • Background with Rust, C++, or other systems languages.

  • Familiarity with deployment patterns: Kubernetes, hybrid cloud, on-prem, or air-gapped environments.

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!


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Skills Required

  • 5+ years of pre-sales or solutions engineering experience
  • Proven experience running technical qualification and proof-of-concept processes within live sales cycles
  • Experience designing or operating distributed systems, search infrastructure, or production-scale data pipelines
  • Strong communication skills across technical and executive audiences
  • Background in data infrastructure or systems architecture
  • Hands-on experience with retrieval-augmented generation pipelines
  • Working knowledge of LLMs, embedding models, and agentic frameworks
  • Working knowledge of MEDDICC or MEDDPICC sales qualification frameworks
  • Experience with vector search, approximate nearest neighbor algorithms, or semantic retrieval systems
  • Background with Rust, C++, or other systems languages
  • Familiarity with Kubernetes, hybrid cloud, on-premises, or air-gapped deployment environments
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The Company
HQ: Berlin

What We Do

Qdrant is an open-source vector database and search engine designed for AI applications. It enables fast, scalable similarity search for unstructured data, supporting use cases like semantic search, recommendation engines, and RAG systems. Built in Rust, it provides high-performance retrieval with metadata filtering and real-time updates, serving as a critical infrastructure component for developers building AI-powered platforms.

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