Note: The job is a remote job and is open to candidates in USA. Soum is building an AI native layer across their C2C marketplace, focusing on enhancing customer interactions and automated processes. They are seeking a Senior GenAI Solutions Engineer to design and implement production-grade AI systems that will impact millions of users throughout their buying and selling journey.
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
- 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
Skills
- 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
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