Principal AI Engineer

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Singapore

Salary: SGD $220,000 - $280,000 base + bonus + equity 

Overview

We are supporting a market-leading technology organisation building advanced AI systems deployed across financial infrastructure, intelligent automation, and large-scale digital platforms. This role is designed for a principal-level engineer who operates comfortably between research and production, shaping architectures that influence how enterprises adopt artificial intelligence. You will enter an environment where engineering depth is expected, autonomy is as standard, and technical decisions decide the future.

Key Responsibilities

• Architect and deploy production-grade AI systems at enterprise scale
• Lead the design of model pipelines spanning ingestion through inference
• Translate complex business problems into structured machine learning solutions
• Establish engineering standards across experimentation, evaluation, and deployment
• Provide technical leadership to senior engineering teams
• Evaluate emerging AI tooling with a pragmatic, production-first mindset

Tech Stack

• Languages: Python
• Frameworks: PyTorch, TensorFlow
• Infrastructure: Kubernetes, Docker
• Cloud: AWS or GCP
• MLOps: MLflow, Kubeflow, or equivalent
• Focus Areas: LLMs, distributed training, retrieval architectures

Ideal Background

• Experience building and shipping machine learning systems
• Proven experience designing scalable AI architectures
• Background within product-led technology firms or advanced engineering teams
• Strong system design capability with production credibility
• Able to influence senior stakeholders while remaining technically hands-on

Why Join

• Operate inside one of Asia’s most technically mature ecosystems
• Influence AI direction at organisational level
• Work alongside senior engineers who prioritise depth over theatre
• Compensation aligned with global technology firms

Bangkok
Salary: THB 5,500,000 - 7,000,000 total compensation 

Overview

We are supporting a global technology firm establishing a major cloud engineering hub in Southeast Asia. This role is built for a director-level leader capable of scaling platform capability while remaining grounded in technical strategy. You will shape infrastructure relied upon by multiple engineering organisations, influencing reliability, developer velocity, and long-term architectural direction.

Key Responsibilities

• Define and execute long-term cloud platform strategy 
• Build and lead senior engineering teams
• Establish standards across automation, security, and cost governance
• Partner with executive leadership on infrastructure investment
• Oversee large-scale migrations and modernisation programmes
• Develop leadership capability to support organisational scale

Tech Environment

• Cloud: AWS-led enterprise environments
• Infrastructure: Terraform, infrastructure as code
• Containers: Kubernetes
• Focus: Observability, resilience engineering, security automation

Ideal Background

• Extensive experience within high-scale cloud environments
• Several years leading senior engineers or platform organisations
• Background in global technology firms or heavily engineered platforms
• Strong commercial awareness around infrastructure spend
• Able to balance engineering ambition with operational discipline

Why Join

• Lead one of the region’s most strategically important engineering hubs
• Operate with genuine organisational influence
• Executive-level compensation aligned with global expansion
• Shape a platform that likeminded engineers choose to join

Sydney  

Salary: AUD $230,000 - $300,000 base + bonus + equity 

Overview

We are partnered with a globally recognised software company expanding its machine learning capability across Asia-Pacific. This position suits a staff-level engineer ready to own high-impact systems that directly power customer-facing products and help define how machine learning integrates into core architecture rather than existing as an experimental function.

Key Responsibilities

• Design and productionise machine learning models within live applications
• Own the lifecycle from feature engineering through deployment
• Build scalable training and inference pipelines
• Improve model performance through structured experimentation
• Partner with data engineers to refine datasets
• Act as a senior technical reference point across ML teams

Tech Stack

• Languages: Python
• Frameworks: PyTorch, TensorFlow, JAX
• Data: Spark, Snowflake, BigQuery
• MLOps: MLflow or Vertex AI
• Infrastructure: Docker, Kubernetes
• Cloud: AWS or GCP

Ideal Background

• Strong software engineering foundations prior to specialising in ML
• Experience deploying models into production environments
• Comfortable balancing experimentation with delivery speed
• History of operating inside product-driven organisations
• Confident collaborating with cross-functional engineering teams

Why Join

• Build ML systems that reach users quickly
• Join an engineering culture centred on ownership