Source description
About the role
Required
5+ years building production software , with at least 2 years shipping LLM and ML-powered features at scale.
Strong Python for backend services, scripting, data pipelines, and ML tooling.
Working ability in TypeScript and React for integrating AI features directly into product surfaces.
Production experience with major LLM providers (Gemini, Claude, GPT) covering tool and function calling, structured outputs, streaming, prompt caching, and cost control.
Deep understanding of Retrieval Augmented Generation (RAG) : document ingestion, chunking, embedding generation, vector databases (pgvector, Pinecone, Weaviate, or similar), hybrid retrieval, and reranking.
Solid grounding in embeddings and vector search : dense vs. sparse representations, similarity metrics, indexing strategies (HNSW, IVF), and dimensionality tradeoffs.
Strong ML and NLP fundamentals : transformer architectures, tokenization, fine-tuning vs. prompting tradeoffs, classification, ranking, and evaluation methodology.
Experience with scripting and automation for data preparation, model evaluation harnesses, and offline analysis.
Comfortable with relational databases (PostgreSQL), caching layers (Redis), REST, SSE, WebSockets, and event-driven architectures.
Production experience with observability, A/B testing, and rolling out model changes safely.
Nice to Have
Experience with recommendation systems, learning to rank, or search relevance at scale.
Marketplace or C2C background covering buyer and seller dynamics, disputes, fraud, and payouts.
Multimodal model experience, including vision and image understanding for listings.
Arabic NLP or bilingual product experience.
Experience with agent frameworks (LangGraph, custom orchestrators) and the judgment to know when to use them.
Fine-tuning, LoRA, distillation, or hosting open weight models in production.
Open source contributions to LLM tooling, eval frameworks, or retrieval libraries.
More at Soum