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Senior AI Engineer

Hmlet

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What AI roles keep asking for: LLMs (30%), Python (28%), AWS (21%), Generative AI (18%), Machine learning (16%), Observability (15%), RAG (13%) — counted across their open postings here.

AI Engineer jobs in Singapore · Remote AI Engineer jobs · LLMs jobs

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Questions you are likely to be asked

  1. Why do you want to join Hmlet?
  2. What is your experience with LLMs? Tell me one thing you learned the hard way.
  3. Walk me through a model you built, from the data to how it was used.
  4. How did you know your model was actually good, and not just good on your test set?
  5. Tell me about a time the data was messy or wrong. What did you do?

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## About the Role We're hiring a Senior AI Engineer to own AI-powered automation end to end — from identifying manual, repetitive, or error-prone work across the business, to building and shipping automated systems that replace it in production. This role sits on top of an existing internal operations platform and works directly with the business stakeholders (across multiple regional markets) who currently do this work by hand. This is not a research role, and not a prototype-and-handoff role. You'll take a defined business problem — a manual process, a spreadsheet, a support queue — through to a shipped, monitored production system, and you'll set the technical standard for how AI gets built across the company going forward. ## What You'll Do - Own AI-powered automation end to end: take a business problem from an undefined state through to a shipped, monitored production system. - Work directly with operations and business stakeholders across multiple regional markets to identify automation opportunities grounded in real operational data. - Design and ship a conversational AI / chatbot layer for customer/resident-facing support, targeting a significant share of time currently spent on repetitive manual support. - Extend self-service capabilities into adjacent flows as the platform matures — bookings, maintenance/service requests, account and billing queries. - Automate document- and data-heavy manual work: data extraction, lifecycle processing (e.g. onboarding/offboarding), pricing and availability logic currently run by hand across disconnected spreadsheets and tools. - Build anomaly detection and decision-support tooling where it has clear financial impact (e.g. exception patterns, pricing anomalies, chargebacks). - Own model selection, prompt/agent architecture, and evaluation practice for anything AI-powered in the company. - Decide what's core AI infrastructure versus a one-off script, and what needs guardrails before it touches a real customer or a real contract. - Make the call on when an LLM is the right tool — and when it isn't. ## What We're Looking For - Strong software engineering fundamentals and a track record of shipping AI-powered features to production — not only notebooks or demos. - Hands-on experience building with LLMs: prompting, agents, and retrieval, along with sound judgment on when fine-tuning (or a non-AI solution) is the better fit. - Demonstrated ability to own a feature from an undefined business problem through to a monitored production system without a pre-written spec. - Strong stakeholder skills — able to work directly with non-technical business stakeholders to translate an existing manual process into a buildable specification. - Comfortable operating in a small team where supporting infrastructure is still being built out. ## Nice to Have - Prior conversational AI / chatbot production experience. - PropTech, real estate, or other operationally complex industry exposure. - Experience operating inside a multi-entity or post-M&A organization. **Location:** Remote (working hours overlapping Japan & APAC) **Type:** Contract **Compensation:** Market-standard, commensurate with experience and skill level

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Listed on wellfound · posted 2026-09-15. ApplySarthi collects openings and links to application pages; the role is advertised by Hmlet, not by us.