- Develop and refine a personalized event recommendation engine.

- Build and optimize embedding-based similarity models using tools like sentence-transformers.

- Integrate LLMs, LangChain, or retrieval systems to create an intelligent event chat interface.

- Experiment with ranking algorithms, content-based filters, and user engagement signals.

- Collaborate closely with a small, agile team of designers, product owners, and developers.

- Own systems from prototype to deployment, with freedom to explore and iterate fast.

Requirements :

- 3-6 years of experience in AI/ML/NLP, with a focus on recommendation systems.

- Strong Python engineering background.

Hands-on with :

1. Embeddings and vector similarity (e.g., cosine similarity)

2. Event-driven or content-based recommenders

3. Text ranking, transformers, or vector DBs (e.g., FAISS, Pinecone).

- Experience building from scratch or scaling AI systems.

Nice-to-Have :

- Exposure to LLMs, LangChain, or conversational AI.

- Graph-based models or social feed architectures.

- Full-stack experience (FastAPI, React, etc.)

- Previous work with semantic search or user behavior modeling