AI Engineer

3echo Pte Ltd

SGhybrid$72k-$96k/yrPosted Jul 18, 2026
Posting intelligenceActively listedReposted 29×, possible evergreen/ghost posting

Skills

typescriptpostgressupabasedockergithubpythonreactgooglecloudllm

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.

Questions about this role

Click "Apply with AI Applyd" above. We auto-fill the application from your resume and answer screening questions in seconds. No copy and paste, no juggling tabs.

Compensation for Machine Learning Engineer roles in Singapore varies widely by seniority, employer size, and remote vs onsite arrangement. Check the salary range on this listing when published, or browse our Machine Learning Engineer hub for Singapore medians across recent openings.

Most applications complete in under 90 seconds. You can track the status in your dashboard and watch the screenshot proof land the moment the application submits.

AI Applyd supports Greenhouse, Lever, Ashby, Workday, iCIMS, SmartRecruiters, Personio, Teamtailor and other major ATS platforms. If we can submit through the platform, we do.

Want AI Applyd to auto-apply to roles like this?

We tailor your resume per posting, fill the forms, and track replies for you.