Sourcing Guide

How We Evaluate AI Infra Engineers

For recruiters, talent partners, and clients

What This Role Is (and Isn’t)

An AI Infrastructure Engineer builds the systems that make ML models work in production. They sit between the ML research team and the platform/DevOps team. They don’t train models — they build the pipelines, serving infrastructure, and orchestration that make models usable.

This Role IS

Building model serving APIs and inference pipelines
Designing evaluation and benchmarking infrastructure
Setting up training pipelines with proper orchestration
Container orchestration for ML workloads
Data pipeline architecture for ML systems
Infrastructure-as-code for cloud ML resources

This Role IS NOT

ML research or model development
Data science or analytics
General backend engineering (no ML context)
DevOps/SRE with no ML experience
Jupyter notebook work or experimentation
Frontend or product engineering

Where to Find Candidates

Don’t search for “AI Infrastructure Engineer” — that’s not how people describe themselves. Search for the work they’ve done and the companies they’ve worked at.

Target Companies (APAC)

Engineers from these companies have the right skill intersection by default:

Medical AI: Lunit (Korea), Qure.ai (India), aetherAI (Taiwan), Infervision (China), DeepTek (India)
ML Platforms: Grab ML Platform, Gojek/Goto, Sea Group/Shopee, ByteDance ML Infra
Workflow/Infra: APAC offices of Uber, Stripe, Snap, Coinbase (heavy Temporal/Cadence users)
AI Startups: Any Series A-C AI company in TW, SG, KR, IN with >10 engineers
Cloud AI: Google Cloud AI, AWS SageMaker, Azure ML teams in APAC

LinkedIn Search Strings

Use these as starting points, not exact matches:

"ML infrastructure" OR "ML platform" OR "model serving" OR "ML engineering"
  + Python + (PyTorch OR TensorFlow)
  + (Taiwan OR Philippines OR Singapore OR India OR Korea)

"machine learning engineer" + (infrastructure OR platform OR pipeline OR deployment)
  + (Docker OR Kubernetes OR Terraform)

"AI engineer" + (production OR serving OR pipeline)
  + (Temporal OR Airflow OR Prefect OR Kubeflow)

Communities & Channels

MLOps Community (mlops.community) — Slack group, 15K+ members
r/mlops and r/MachineLearning — Reddit
Temporal Community — Slack + forum (temporal.io/community)
PyTorch Forums — deployment and serving categories
APAC ML meetups — ML Tokyo, ML Singapore, Taiwan AI Academy alumni

Screening Criteria

Score candidates on these five dimensions. A strong candidate scores 3+ on at least four.

Dimension

1 — Weak

3 — Good

5 — Exceptional

Systems Python

Mostly notebooks, scripts. No packaging or testing.

Writes production Python. Uses async, typing, pytest. Understands packaging.

Designs Python systems. Custom frameworks, performance profiling, C extensions when needed.

Production ML

Trained models but never deployed them. No serving experience.

Has deployed models to production. Built serving APIs, handled versioning, monitoring.

Built ML platforms used by multiple teams. Model registry, A/B testing infra, automated retraining.

Infrastructure

Uses cloud console. No IaC. Manual deployments.

Terraform/Pulumi. Docker + K8s. CI/CD for ML. Understands cost and compliance.

Designs multi-tenant infra. Per-customer isolation. HIPAA/SOC2 compliance. Autoscaling ML workloads.

Orchestration

Cron jobs. No proper pipeline tooling.

Uses Airflow/Prefect/Temporal. Retry logic, dependency DAGs, caching.

Designs orchestration systems. Custom executors, distributed workflows, multi-step ML pipelines with rollback.

Startup Fit

Needs detailed specs. Waits for direction. Big-company habits.

Self-directed. Scopes own work. Ships incrementally. Communicates proactively.

Founder-mentality. Owns outcomes end-to-end. Makes architectural decisions. Unblocks themselves.

Interview Process

Three steps, each with a clear signal we’re looking for:

Step 1: Resume Screen (5 min)

Has built something in production with ML frameworks (not just trained models)
Infrastructure keywords: Docker, K8s, Terraform, CI/CD, cloud (not just “familiar with”)
Python as primary language (not Java/Go with some Python scripting)
Work at companies where ML infra matters (see target companies above)

Red flags: Only research/academic experience. Only notebook/Kaggle work. “Full stack” with no ML depth. Resume lists every technology ever invented.

Step 2: Technical Screen (30 min, async or live)

Ask them to walk through a system they built. Specifically:

“Describe an ML system you built end-to-end. What was the architecture? What broke? How did you fix it?”
“How did you handle model versioning and deployment? What happened when a model update caused a regression?”
“Walk me through how you’d design a pipeline that takes a model upload, runs evaluation across 5 benchmarks, and reports results — with caching and retry logic.”

What to listen for: Specificity (not hand-wavy), trade-off awareness (not just “best practice”), debugging stories (not just success stories), systems thinking (not just component thinking).

Step 3: Trial Project (2-4 weeks, paid)

Real work with the client team. Scoped deliverable, clear success criteria. This is where you see how they actually work — communication, code quality, ability to operate in ambiguity.

Compensation Benchmarks

Ranges for AI Infrastructure Engineers by geography and seniority (USD, monthly, full-time equivalent):

Mid-level (3-5 years)

Philippines: $2,500 — $4,000
Taiwan: $3,500 — $5,500
Singapore: $5,000 — $7,500
India: $2,500 — $4,500

Senior (5-10+ years)

Philippines: $4,000 — $6,000
Taiwan: $5,500 — $8,000
Singapore: $7,500 — $12,000
India: $4,000 — $7,000

Hourly: divide monthly by 160 for approximate rate

These are talent salary ranges, before Worca margin. Adjust based on specific domain expertise (medical AI, semiconductor = premium).

Common Mistakes

Sourcing on job boards — The best AI infra engineers are employed and not looking. Outbound on LinkedIn targeting specific companies is 10x more effective than job postings alone.
Filtering on exact tech stack — A strong engineer who knows Airflow can learn Temporal in a week. Filter on systems thinking, not tool names.
Confusing ML researchers with ML engineers — PhD + publications does not mean they can build production systems. Ask about deployments, not papers.
Ignoring the “moonlighter” pool — For hourly/contract roles, the best candidates often have a full-time job and want interesting side work. Pitch the project, not the job.
Screening for seniority by years — A 3-year engineer at Grab ML Platform may be stronger than a 10-year engineer at a non-ML company. Screen for what they’ve built, not how long they’ve been building.