Our client is a new stealth startup in Indonesia that are currently looking for a Founding Engineer with hands-on GenAI builder experience to ship LLM-powered customer support automation (RAG + LangChain + WhatsApp/Shopee APIs) from scratch.
Key Requirements:
Proven track record building and shipping LLM-powered applications end-to-end (not just research/POCs).
Expertise with RAG pipelines, LangChain or LlamaIndex, and vector databases (Pinecone, Weaviate, FAISS, Milvus).
Strong Python development (FastAPI, PyTorch/TensorFlow, Hugging Face).
Experience integrating LLMs into production systems (chatbots, copilots, customer-facing workflows).
Familiarity with 3rd-party APIs (WhatsApp, TikTok, Shopee, Shopify, Zendesk).
Comfortable with 0→1 building in early-stage environments; able to balance speed with scalability.
CS/adjacent degree or equivalent coding expertise; strong problem-solving and debugging skills.
Stability in past roles; clear evidence of recent hands-on builds.
Willing to start hands-on coding full-time, with a path to CTO as the company scales (hiring/managing engineers, shaping tech vision).
Key Responsibilities:
Design & Build the AI Core
Develop the intelligence layer using LLMs (OpenAI, Anthropic, Gemini, etc.) for e-commerce customer support workflows (order tracking, refunds, cancellations, shipping status).
End-to-End Automation Engine
Implement RAG pipelines, vector search, and conversational flows.
Build automation that can independently query APIs (Shopee/TikTok/WhatsApp) and execute actions like refunds, order updates, etc.
Integrate with Commerce Platforms
Connect AI workflows with e-commerce APIs (example: Shopee, Tokopedia, TikTok Shop) and messaging platforms (WhatsApp Business).
Build the User Experience for Sellers
Develop the interface where sellers can:
Monitor AI conversations.
Intervene when needed
Manage customer interactions in one place.
Product Iteration & Feedback Loops
Deploy MVP with first e-commerce pilot users.
Collect data & feedback to refine conversational accuracy, workflow automation, and user trust.
Technical Leadership
Define the AI technical vision & architecture.
Own decisions on LLM frameworks, vector DBs, orchestration tools.
Establish coding standards, infra setup, and security.
Future Scaling
Transition from sole contributor → team leader / CTO.
Recruit, mentor, and lead engineering hires as company scales.