Our Company is expanding its AI initiatives and is seeking an AI Product & Engineering Lead with prior AI startup or founding experience to drive the design, delivery, and scaling of real-world LLM-powered systems.
This role goes beyond pure engineering execution. You will play a key role in technical decision-making, product shaping, and system architecture, translating cutting-edge AI capabilities into scalable and commercially viable solutions.

Key Responsibilities

AI System Ownership & Delivery

• Own or co-own AI initiatives from problemdefinition, technical strategy, architecture design, to production deployment.
•  Make informed decisions on model selection, RAGarchitecture, agent orchestration, and system trade-offs under real-worldconstraints.
• Design and optimize LLM inference pipelines,embeddings, and system performance for reliability and scalability.

Product-Oriented AI Engineering

• Collaborate closely with business, product, and leadership teams to translate ambiguous requirements into AI-driven solutions.
• Design structured, reusable Prompt Engineering and agent workflows to ensure controllability, robustness, and explainability.
• Evaluate build vs. buy decisions across models, agent platforms, and infrastructure.

Multi-Agent Systems & Reasoning

• Design and orchestrate multi-agent systems using frameworks such as LangChain, LangGraph, MCP, or equivalent.
• Implement reasoning paradigms including ReAct, Chain-of-Thought (CoT), Tree-of-Thought (ToT).
• Assess and integrate agent platforms (e.g., Coze, Dify, FastGPT) when appropriate.

RAG & Knowledge Infrastructure

• Design and iterate on Retrieval-Augmented Generation (RAG) architectures.
• Build and optimize knowledge systems using vector databases such as Milvus, FAISS, or Chroma.
• Continuously improve retrieval quality, context grounding, and reasoning accuracy.
• Track emerging AI trends in model alignment, agent systems, and multimodal AI.
• Contribute to internal standards, documentation, prototypes, and technical decision frameworks.
• Mentor engineers or collaborate with external partners when needed.

Qualifications

• Bachelor’s degree or above in Computer Science, AI, or a related field.
• Previous experience founding, co-founding, or being an early technical member of an AI startup, or leading AI products in a startup environment.
• Proven delivery of at least one end-to-end AI product (LLM / RAG / Agent-based system) in production.
• Strong hands-on experience with LLMs, Prompt Engineering, RAG pipelines, and agent frameworks.
• Solid understanding of ReAct-style agent workflows and multi-agent system design.
• Experience making technical decisions under uncertainty, cost, and time constraints.

Nice to Have

• Experience with LoRA / QLoRA, model alignment, or inference optimization.
• Exposure to AI product commercialization, user feedback loops, or go-to-market iteration.
• Open-source contributions, technical writing, or public speaking.
• Strong cross-functional communication and leadership skills.

This is a global role, open to candidates worldwide.