Machine Learning Engineer
Summary
An MLOps/platform engineer role on an AI team in Singapore: the person builds end-to-end ML pipelines, operationalizes LLMs, RAG and multi-agent systems, and manages the model lifecycle from experimentation to production, using Python, Docker, Kubernetes and CI/CD tooling.
We are seeking a skilledmachine learning platform engineer (MLOps) to join our agile platform teamwhich is part of our ML & AI ART. You drive the orchestration of advancedagentic workflows to enable autonomous, AI-driven systems. You will beresponsible for engineering robust data pipelines, establishing comprehensivemodel management lifecycles, overseeing all foundational platform-level AIintegrations – including engineering a robust library of AI skills for agentuse.
Design, develop and deploy machine learningsolutions and services
- Implement end-to-end machine learningpipelines from data ingestion to training and model serving Operationalize LLMs, embeddings, andmulti-agent systems in real-world applications
- Manage the machine learning and modellifecycle (experimentation, registry, deployment)
- Oversee the model promotion lifecycle,coordinating validation gates and approval workflows to safely deploy new modelversions from stating to production
- Containerize applications using Docker andorchestrate them via Kubernetes
- Build and maintain CI/CD pipelines for MLmodels and LLM applications
- Design and implement production grade RAGsystems
- 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)
