MLOps Engineer

Responsibilities

• Design and develop platforms for AI model training and inference
• Develop and operate container-native workflows for training, inference, and monitoring
• Develop and operate microservices on Kubernetes using microservice architecture
• Optimize ML/DL models across various domains and deploy them to production

Requirements

• Experience developing AI/ML services or platforms in public cloud environments
• Proficiency in Git and Docker; hands-on experience with Kubernetes and IaC tools such as Terraform and Helm
• Experience operating CI/CD with Argo Workflows and Argo CD
• Understanding of ML/DL fundamentals and ability to design systems for training and inference workloads
• Backend development experience in Python or Golang with web frameworks such as FastAPI or Echo
• Ability to explain complex technical concepts and collaborate across teams to define requirements and solutions

Preferred Qualifications

• Experience rapidly evaluating new technologies and validating them through PoC
• On-premise service development and operations or large-scale traffic handling experience
• Understanding and implementation experience with API Gateway and Inference Gateway
• Experience with LLM serving, LLMOps, and optimized operations
• Experience building AIOps or AI agents using logs, metrics, and traces

About the Company

LG AI Research is the AI research division of LG, building MLOps pipelines and infrastructure to optimize the full lifecycle of AI models from development to production deployment.