AI Platform Engineer: End-to-End ML Pipelines & LLMs

Tech Aalto Pte ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Tech Aalto Pte ltd is seeking an AI engineer to bridge research and production, building robust data pipelines, model registries, and production-grade AI integrations. You will work across data scientists and engineers to operationalize models and agentic workflows in real-world systems.

The role focuses on developing, deploying, and scaling ML solutions, managing lifecycle stages, and ensuring reliability, performance, and governance of AI workloads within a fast-paced agile environment.

Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• Advanced proficiency in Python programming with clean, testable code.
• DevOps & Containers: Docker and Kubernetes experience.
• Experience with ML lifecycle tooling like MLflow or weights & biases.
• Experience with LLMs and AI agent workflows.

Responsibilities

• Design, develop and deploy ML solutions and services.
• Operationalize end-to-end ML pipelines from data ingestion to training and model serving.
• Operationalize LLMs, embeddings, and multi-agent systems in real-world apps.
• Manage ML lifecycle: experimentation, registry, deployment.
• Oversee model promotion lifecycle with validation gates and approvals.

Skills

Problem solving

Communication

Project management

Continuous learning

Attention to detail

Education

Bachelor's or Master's in Data Science/CS/Math/Stats

Tools

Docker

Kubernetes

MLflow

Weights & Biases

GitLab/Jenkins

Job description

Tech Aalto Pte ltd is seeking an AI engineer to bridge research and production, building robust data pipelines, model registries, and production-grade AI integrations. You will work across data scientists and engineers to operationalize models and agentic workflows in real-world systems.

The role focuses on developing, deploying, and scaling ML solutions, managing lifecycle stages, and ensuring reliability, performance, and governance of AI workloads within a fast-paced agile environment.