Senior Solutions Engineer
Job Description
Key Skills Required
Master these to land this role
Want to know if you're a match for this job?
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 candidates, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy.
How would you rate this job post?
See what other professionals think about this role.
Similar Opportunities
More Openings at Qdrant
Explore Top Companies in this Space
Corelight
Network Detection & Response (NDR) / Enterprise Cybersecurity Telemetry / Threat Hunting & Security Operations SaaS / Open-Source Security Infrastructure
Linux.org
Open-Source Software / Developer Tools / Operating Systems / Community Platforms
GetGround
Real Estate / PropTech / Finance / SaaS
Cognite
Industrial IoT / AI / Enterprise Software / Energy & Utilities
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.
Safety First
- Never pay for a job application.
- Do not share sensitive bank info.
- Verify the client before starting work.








