AI / GenAI Solutions Engineer

Role Overview

• Architect production GenAI systems across multiple domains, including conversational agents, automated order and dispute workflows, personalized discovery and recommendations, content generation, search relevance, and emerging use cases.
• Own features end-to-end, from problem framing and model selection through backend services (FastAPI, Python), frontend integration (React, TypeScript), evaluation, deployment, and monitoring.
• Design agentic workflows with tool calling, multi-step reasoning, retrieval augmented generation, and integrations with internal APIs, third-party SaaS, and event-driven systems.
• Build the retrieval and embeddings stack, including chunking strategies, embedding model selection, vector indexes, hybrid search, reranking, and retrieval evaluation pipelines.
• Make it reliable and cost-efficient through streaming, prompt caching, latency budgets, token cost optimization, observability for LLM calls, and graceful fallback when models or upstreams misbehave.
• Establish evaluation rigor with offline and online evals covering response quality, tool call correctness, hallucination rate, retrieval precision, and business KPIs.
• Drive experimentation and research by evaluating new models, frameworks, and agent patterns, running focused experiments, and bringing what works into production.
• Mentor and raise the bar for engineers across the team on AI and ML best practices, prompt engineering, and production readiness.
• Partner cross-functionally with Product, Engineering, Data, Ops, and CX to identify high-leverage AI opportunities and ship them.

Requirements

• 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.

Tech Stack

• Open Source
• Postgres
• Python
• React
• Redis
• TypeScript