Senior Solution Engineer - APJ

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.

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.

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.

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