GenAI Platform Architect Lead

SATS Ltd.; Singapore; On-site; SGD 180,000 - 320,000; Full time

Job summary

SATS Ltd. is seeking a Lead AI Platform Engineer to design, build, and operate a production-grade AI/GenAI platform.

You will architect scalable ML pipelines, model-serving infra, and RAG workflows while mentoring others and collaborating with data scientists to deploy models at scale. You will own cloud infrastructure across AWS/Azure/GCP, implement IaC, CI/CD for ML workloads, and optimize cost and performance in a dynamic aviation logistics domain.

Qualifications

• Bachelor's degree in Computer Science, Mathematics, or related field.
• 10–12 years in software engineering or GenAI Platform Engineering.
• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP).
• Strong Python and Docker/Kubernetes skills.
• Experience building production-grade AI platforms and GenAI services.

Responsibilities

• Design and build scalable ML/GenAI pipelines (batch & real-time).
• Build and operate model-serving infrastructure and optimize inference latency.
• Develop RAG pipelines, LLM orchestration, and multi-agent workflows.
• Maintain vector databases and embedding pipelines.
• Create and unify AI Platform components (API gateway, auth, tracing).
• Lead architecture reviews and enforce coding best practices.
• Collaborate with data scientists and AI Engineers to productionize models.

Skills

Python

Cloud platforms

Docker

Kubernetes

Terraform/IaC

CI/CD

MLflow

Agentic AI

Education

Bachelor's degree in Computer Science, Mathematics, or related field

Tools

Docker

Kubernetes

Terraform

MLflow

Job description

SATS Ltd. is seeking a Lead AI Platform Engineer to design, build, and operate a production-grade AI/GenAI platform.

You will architect scalable ML pipelines, model-serving infra, and RAG workflows while mentoring others and collaborating with data scientists to deploy models at scale. You will own cloud infrastructure across AWS/Azure/GCP, implement IaC, CI/CD for ML workloads, and optimize cost and performance in a dynamic aviation logistics domain.