Machine Learning Engineer

STRT.ASIA PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

STRT.ASIA PTE. LTD. is seeking a skilled ML platform engineer (MLOps) to join our agile platform team within our ML & AI ART division in Singapore.

You will drive orchestration of advanced agentic workflows to enable autonomous AI-driven systems and engineer robust data pipelines with comprehensive model management lifecycles. You will operationalize LLMs, embeddings and multi-agent architectures, containerize applications with Docker, orchestrate via Kubernetes, and build CI/CD pipelines for ML

Qualifications

• Proficient in Python, Docker, and Kubernetes to build and deploy ML solutions.
• Experience with end-to-end ML pipelines from data ingestion to training and serving.
• Familiarity with CI/CD for ML models and LLM applications.

Responsibilities

• 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 ML lifecycle (experimentation, registry, deployment).
• Oversee model promotion, validation gates, and production deployment.
• Containerize applications using Docker and orchestrate with Kubernetes.
• Build and maintain CI/CD pipelines for ML models and LLM apps.
• Design and implement production-grade RAG systems.
• Write clean, testable, efficient Python code.

Skills

Python

Docker

Kubernetes

CI/CD

MLflow

LLMs

Data preprocessing

Feature engineering

Model lifecycle

Agentic workflows

LLM orchestration

GPU compute

Cloud compute

GitLab/Jenkins

Tools

Weights & Biases

GitLab

Jenkins

Job description

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 servingOperationalize 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)