AI Engineer
Skills
About the role
AI Engineer
3echo Pte Ltd · Singapore · Full-time · On-site/hybrid SGD 6,000–8,000 per month
About 3echo
3echo is a Singapore-based AI company building agent-powered products for businesses across Southeast Asia. We deploy AI agents that do real work - content production, operations, client delivery - measured by business outcomes, not features shipped. Small team, founder-led, fast iteration, paying clients.
The role
You're an AI Engineer who ships end to end. Not a prompt tinkerer, not a pure backend dev - you sit where agent workflows, generative media pipelines, and the client's actual business outcome meet. You'll work directly with the founder in a small, fast team where your code goes to paying clients in days, not quarters.
Mid-level: 2–4 years shipping real systems. What matters more than years is that you've built things people use - and that Claude is how you work. We're a Claude-native shop: Claude Code is the primary development environment, MCP is the integration layer, and agent skills are how we encode process. One engineer here ships what a team of five ships elsewhere, because the leverage is the tooling. If you're not already working this way, you should be hungry to.
What you'll own
1. Agent infrastructure
Build and maintain MCP servers, agent skills, and workflow orchestration that power client deployments
Encode business rules - budget limits, approval checkpoints, usage metering, sign-off flows - as self-enforcing agent behavior
Automation pipelines in n8n connecting internal systems, comms, and client channels
2. Generative media pipeline
Operate and extend our image and video generation pipeline: frontier models, reference-anchored character consistency, multi-stage production workflows
Cost-aware generation: quota management, usage metering, resolution strategy, provider failover
QA tooling for identity consistency, brand compliance, and output quality
3. Full-stack product & client delivery
Backend services in Python/FastAPI and TypeScript, Postgres/Supabase, deployed on GCP Cloud Run
Client-facing surfaces in React where the product needs a UI
Forward-deployed work: onboarding clients, debugging live jobs, turning client feedback into shipped fixes fast
Our stack
Claude & Claude Code · MCP · n8n · frontier image/video generation models (incl. Seedance 2.0 via BytePlus) · Gemini via OpenRouter · Python/FastAPI · TypeScript/React · Postgres/Supabase · Docker · GCP Cloud Run · on-prem RTX 5090 GPU servers
You don't need to know all of it. You need to be dangerous in most of it within your first month.
You've probably done some of this
Shipped an LLM-powered product or agent system to real users (side projects count if they're live)
Built with tool-calling / MCP / function-calling APIs, not just chat completions
Worked with generative image or video APIs and dealt with their failure modes
Owned a backend service in production - deploys, logs, incidents, the unglamorous parts
Ship daily with Claude Code (or equivalent) as your primary environment - you know how to scope tasks for an agent, review its output critically, and get 5–10× throughput from it, not 1.2×
Written system prompts, skills, or MCP tools that made a model reliably do a job - you treat the model as a system to engineer, not a chatbot to talk to
How we hire
We hire on AI DNA: attitude, learning curve, hunger. Certifications and brand-name employers don't move us. A GitHub repo, a live demo, or a story about the hardest thing you shipped moves us a lot. Attitude disqualifies faster than ability qualifies.
Part of the interview is watching you work with Claude on a real task. We're not testing whether you can code without AI - we're testing how much more you can do with it.
First 90 days - what success looks like
Week 2: shipped your first fix to a live client system
Month 1: own one of the three pillars day-to-day with the founder reviewing, not driving
Month 3: a client outcome (turnaround, first-pass rate, or cost-per-deliverable) measurably improved by something you built
Compensation & logistics
SGD 6,000–8,000 per month
Singapore-based; Citizens, PRs and pass holders considered
Direct exposure to founder-level decisions across product, infrastructure, and clients
How to apply
Email [careers email] with:
A link to something you built (repo, demo, live product)
Three sentences on the hardest technical problem you've solved
No cover letter needed
Job Types: Full-time, Permanent
Pay: $6,000.00 - $8,000.00 per month
Benefits:
Health insurance
Professional development
Work Location: In person
Compensation
This Machine Learning Engineer role pays $72k-$96k/yr. Within typical range for machine learning engineer roles in Singapore.
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