Senior Solution Architect

Qdrant is a cutting-edge technology company specializing in open-source vector databases designed for next-generation AI applications. Our platform enables high-performance vector similarity search, allowing businesses to unlock deep insights from unstructured data. By offering both open-source software and managed cloud solutions, we empower developers and enterprises to build scalable, AI-driven search and recommendation systems with ease.

Tasks

We are seeking an experienced Senior Solution Architect with a strong background in databases, search engines, and machine learning. In this pivotal role, you will collaborate closely with our clients to design and implement innovative solutions leveraging Qdrant's vector database technologies. You will serve as a technical expert and trusted advisor, guiding clients through the adoption and integration of our products to ensure their success.

Requirements

Key Responsibilities

• Client Collaboration: Engage with clients to understand their technical requirements and business objectives.
• Solution Design: Architect scalable and efficient solutions utilizing Qdrant's vector database and cloud offerings.
• Technical Expertise: Provide in-depth guidance on best practices for integrating Qdrant's products into existing systems.
• Pre-Sales Support: Assist the sales team with technical presentations, demonstrations, and proof-of-concept projects.
• Workshops & Training: Lead technical workshops and training sessions for clients and partners.
• Cross-Functional Liaison: Act as a bridge between clients and internal teams, including engineering and product development.
• Feedback Contribution: Relay client feedback to influence product enhancements and roadmap.
• Industry Insight: Stay updated on the latest trends in vector search, machine learning, and database technologies.

Qualifications

• Experience: Minimum of 5 years in solution architecture, technical consulting, or a similar role.

• Technical Skills:

• Extensive knowledge of database systems.

• Experience with search engines like Elasticsearch or Solr.

• Proficiency in vector databases and understanding of vector search concepts.

• Strong programming skills in languages such as Python, Go, Rust, or Java.

• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch).

• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).

Preferred Qualifications

• Open-Source Contribution: Experience contributing to open-source projects.
• Industry Experience: Background in AI-powered applications like recommendation systems, NLP, or computer vision.
• Certifications: Relevant certifications in cloud platforms, databases, or machine learning.
• Distributed Systems Knowledge: Understanding of distributed computing and microservices architecture.