AI Engineer

Matrix Process Automation Sdn. Bhd.; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

Matrix Process Automation Sdn. Bhd. in Singapore seeks a skilled machine learning engineer to design, develop, and deploy machine learning solutions. Key responsibilities include implementing end-to-end machine learning pipelines and operationalizing models in real-world applications.

The ideal candidate must have proficiency in Python, experience with Docker and Kubernetes, and hands-on experience with MLOPS tools like MLflow. Join a dynamic team and contribute to innovative machine learning efforts.

Qualifications

• Proficient in developing machine learning solutions and services.
• Experience with containerization and orchestration technologies.
• Advanced proficiency in Python, focusing on clean and efficient coding practices.

Responsibilities

• Design, develop and deploy machine learning solutions and services.
• Implement end-to-end machine learning pipelines.
• Manage the machine learning lifecycle from experimentation to deployment.

Skills

Machine learning solutions development

Containerization using Docker

Orchestration with Kubernetes

Python programming

MLOPS tools

Large Language Models (LLMs)

Tools

Docker

Kubernetes

MLflow

GitLab

Jenkins

Job description

Responsibilities and Qualifications

• Design, develop and deploy machine learning solutions and services
• Implement end-to-end machine learning pipelines from data ingestion to training and model serving
• Operationalize LLMs, embeddings, and multi-agent systems in real-world applications
• Manage the machine learning and model lifecycle (experimentation, registry, deployment)
• Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production
• Containerize applications using Docker and orchestrate them via Kubernetes
• Build and maintain CI/CD pipelines for ML models and LLM applications
• Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code
• DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration
• Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads
• MLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry
• Proven experience working with Large Language Models (LLMs)
• Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns
• Experience with data preprocessing, feature engineering, and model selection and evaluation techniques
• Hands-on experience with CI/CD pipelines (GitLab, Jenkins)