Forward Deployed Architect – AI/LLM

Location: Vietnam/Bangkok Type: Full-time | Remote-friendly within SEA | 30-50% travel to customers Industry: Enterprise AI / Machine Learning / LLMOps Confidential search on behalf of our client – a top-tier AI company deploying LLMs in production for Fortune 500s About the Company Our client builds and deploys production-grade LLM systems for Fortune 500s. No slideware. No 12-month POCs. Forward Deployed Architects embed with customers from Day 1 to ship AI that moves P&L. Their customers stay because the AI works. Churn is near-zero. Now they're building the founding SEA technical team and have retained Newbridge to lead the search. The Role: Forward Deployed, Not Forward Slides You are 50% engineer, 50% trusted advisor, 100% owner of customer outcomes. As a Forward Deployed Architect , you will: Deploy with customers : Embed on-site/virtual with CTOs, VPs of Engineering, Data Science teams. Week 1 = in their repo, not in a workshop Ship production AI : Scope, prototype, and harden RAG, agents, fine-tunes, eval pipelines on customer data. Own it from demo prod Be the technical voice in sales : Partner with Sales Leads. Kill bad deals early. Make good deals unbeatable by proving it works Bridge product customer : Feed real-world edge cases, data issues, and infra constraints back to core Eng. You shape the roadmap Post-sales : You don't throw over the wall. You stay embedded until the customer is getting ROI. Then you help them expand Tech you'll touch : Python, PyTorch/TensorFlow, LangChain/LlamaIndex, vector DBs, Kubernetes, AWS/GCP/Azure, eval frameworks, enterprise data systems. If it's in a modern LLM stack, you'll see it. What Success Looks Like in Year 1 3+ enterprise customers live in production with measurable ROI <60 days average time from kickoff first prod deployment Become the #1 technical reason a CTO chooses us over competitors 2+ new product features shipped because you found the gap Who You Are You're the engineer every Sales Lead wants in the room. And customers ask for by name on the next project. Must-haves: 8+ years as ML Engineer, Software Engineer, Solutions Architect, or Data Scientist in production environments Shipped ML/AI : You've put models/data products in prod, owned uptime, debugging, evals. LLM/RAG experience strongly preferred Full-stack comfort : Strong Python. Can read customer's infra. Can write glue code, spin up a K8s cluster, debug a weird SQL query Customer-facing : You can whiteboard with a CTO, then pair-program with their MLE. You explain tradeoffs clearly Builder mindset : Hate bureaucracy. Love messy 01 problems. It's not in the docs excites you Based in : Singapore, KL, Jakarta, Bangkok, or Manila. Passport + willingness to be with customers 30-50% across SEA Bonus: Forward Deployed experience : Ex-Palantir, C3, Databricks, Scale, OpenAI, Anthropic, or similar Enterprise context : Worked with banks, telcos, governments, conglomerates. Know security/compliance pain Fluent in : Bahasa Indonesia, Thai, Tagalog, or Mandarin + English Open source / writing : Blog posts, repos, talks that prove you can teach Why This Role > Big Tech MLE Impact : Your code goes live in 60 days at the biggest companies in SEA. Not L6 promo packet in 18 months Comp : Top 10% base for SEA + meaningful equity in a rocketship. $180k–$350k USD total comp Team : Engineers from OpenAI, DeepMind, Palantir. No politics. No B-players Growth : Path to founding Eng in your country, Regional Field CTO, or move to core product Learning : See 10 different LLM deployments/year. You'll learn more in 1 year than 3 years in Big Tech