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