What You Will Do

Design and ship tools on our MCP server and agent API so AI assistants (ChatGPT, Claude, Gemini, Meta AI and others) can search, compare, build carts and check out reliably.
Build LLM pipelines that extract, normalise and enrich product data: pack sizes, ingredients, variants, categories and claims, with human review where it matters.
Improve catalogue search and ranking for natural-language requests such as restock lists, substitutions and budget limits.
Create evaluation sets and automated evals that measure whether agents find the right product, price and quantity, and catch regressions before release.
Instrument agent traffic end to end: tool-call logs, latency, failure modes and conversion, and turn what you learn into product fixes.
Keep agent-facing documentation, llms.txt and product feeds accurate so assistants describe Tanatasa correctly.
Work asynchronously with a small team in Singapore, writing clear design notes and pull requests.

What We Are Looking For

3+ years of professional software engineering experience, including at least 1 year building production features with LLMs.
Strong TypeScript or Python, and comfortable reading and writing both.
Hands-on experience with LLM tool calling, structured outputs, embeddings or retrieval, and the judgement to know when not to use a model.
You have built evals or test harnesses for non-deterministic systems and use them to make decisions.
Solid SQL and HTTP API design.
Clear written English and a good async working style: you unblock yourself and keep others informed.
Based in Asia-Pacific with at least 4 hours of overlap with Singapore time (UTC+8).

Built or contributed to an MCP server, or shipped integrations with ChatGPT apps, Claude, Gemini or similar agent platforms.
Ecommerce or marketplace experience: catalogues, search relevance, product feeds or checkout.
Experience with Cloudflare Workers, D1, Vectorize or other edge platforms.
Open-source work or writing that shows how you think about AI systems.

First 30 days

Learn the agent API, MCP tools and catalogue data, set up the eval harness locally and ship your first fixes.

First 60 days

Own one agent capability end to end, such as search quality or product data enrichment, with evals that prove it improved.

First 90 days

Measurably improve how often AI assistants find and buy the right product, and set the roadmap for the next quarter.

Apply Now

Email us with the subject line Application - AI Engineer (Remote). Include anything that shows how you think and the work you have done.

Your resume or CV, or a LinkedIn profile, and the country and time zone you work from.
Links to GitHub, papers, posts or products that show LLM or agent work you did, with a line on your part.
A short note on one eval you would build first to test whether an AI assistant shops Tanatasa correctly.
Your earliest availability and expected salary range, if you are comfortable sharing it.