SOUM - AI / GenAI Solutions Engineer
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. • 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.
Responsibilities
• 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. • 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. • Where You'll Have Impact • A non-exhaustive list of areas we are actively building in or want to: • Conversational AI for customer support, dispute resolution, and seller assistance • Automated order handling, escalation routing, and workflow orchestration • Personalized discovery, recommendations, and search relevance • Listing quality, including auto-generated titles, descriptions, categorization, and image understanding • Trust and safety, including fraud signals, anomaly detection, and content moderation • Internal agent tooling for ops and CX teams
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