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Machine Learning Engineer at STRT.ASIA PTE. LTD. – Singapore | Jobyssey

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Machine Learning Engineer

Machine Learning Engineer

STRT.ASIA PTE. LTD. · Singapore · 2 weeks ago

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Category: ML ai

agentic-ai agile ai ci-cd cloud containerization data-ingestion data-pipelines devops docker embeddings feature-engineering gitlab jenkins

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ML / AI jobs Remote ML / AI jobs Jobs in Singapore ML / AI jobs in Singapore

machine-learning

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)