Responsibilities
- Design, develop, and optimize AI/ML features in Qdrant’s core engine and SDKs.
- Prototype and implement integrations with popular ML frameworks (e.g., Hugging Face, OpenAI, LangChain).
- Analyze performance, identify bottlenecks, and implement scalable solutions in real-world AI pipelines.
- Collaborate with product and engineering teams to define and deliver impactful features.
- Support developers and users via GitHub, Discord, and other community channels.
- Communicate complex technical concepts clearly in customer calls, demos, and onboarding sessions.
- Represent Qdrant at conferences, meetups, and webinars — both as a speaker and technical expert.
- Create educational content such as blog posts, tutorials, sample projects, and documentation.
- Collect feedback and insights from the community to inform product development.
Requirements
- Strong proficiency in Python.
- Solid understanding of machine learning concepts, embeddings, and vector search.
- Experience with at least one modern ML framework (e.g., PyTorch, TensorFlow, Hugging Face).
- Excellent communication skills; ability to explain technical topics to diverse audiences.
- Prior experience contributing to open-source projects or engaging with developer communities.
- Comfortable presenting and participating in public forums, events, or customer meetings.
Nice to Have
- Contributions to AI infrastructure, vector databases, or search systems.
- Experience building ML-powered products in production.
- Knowledge of large language models (LLMs) and retrieval-augmented generation (RAG).
- Public speaking or published technical content (talks, blog posts, tutorials).
- Familiarity with the Qdrant ecosystem or similar technologies.
