MLOps Engineer

1 day ago


Petaling Jaya, Selangor, Malaysia MY E.G. Services Berhad (MYEG) Full time 60,000 - 120,000 per year

We are seeking a Mid–Junior Level MLOps Engineer specializing in ML model orchestration, monitoring, and deployment, with a strong emphasis on LLM (Large Language Model) services. This role will involve deploying and scaling ML pipelines, setting up CI/CD workflows, implementing observability solutions, and ensuring cost-efficient operations across multiple cloud and on-prem environments.

Responsibilities

  • Deploy & Monitor ML Models: Manage the deployment of LLM and chatbot models using Kubernetes, Docker, and cloud-native tools across AWS, Huawei, or GCP.
  • Model Orchestration: Implement ML pipelines using Airflow, Kubeflow, or MLFlow for training, serving, and monitoring workflows.
  • CI/CD for ML Workflows: Set up Git-based CI/CD pipelines using Jenkins or similar tools to ensure reliable deployments and rollbacks.
  • Observability & Monitoring: Implement logging, tracing, and performance monitoring stacks (e.g., Grafana, Prometheus, ELK).
  • Cost Optimization: Continuously optimize resource utilization, scaling strategies, and deployment costs across cloud environments.
  • API & Traffic Management: Configure API gateways (Kong) and load balancing solutions (Kafka, Zookeeper) to ensure reliable and secure model serving.
  • Automation & Scripting: Develop automation scripts and operational dashboards for model health, inference latency, and error tracking.
  • Microservices Architecture: Design and manage microservices-based ML systems for modular, scalable deployments.
  • Data Management: Ensure integration with Postgress DB for metadata storage, logs, and system configuration.

Qualifications

  • Bachelor's degree in Computer Science, Data Engineering, AI/ML, or related fields.
  • 1 – 3 years of experience in MLOps, DevOps, or Cloud Engineering with ML-focused workflows.
  • Experience working with LLM-based ML models is preferred but not mandatory.
  • Cloud & OS: AWS, Huawei Cloud, GCP, Ubuntu/Linux administration.
  • Containerization & Orchestration: Kubernetes, Docker, MLFlow, Kubeflow, Airflow.
  • CI/CD & Version Control: Git, GitHub Actions, Jenkins, GitLab CI/CD.
  • Monitoring & Observability: Grafana, Prometheus, ELK stack.
  • API & Traffic Management: API Gateways (Kong), Load Balancing (Kafka, Zookeeper).
  • Databases & Data Ops: PostgreSQL database integration for ML pipelines.
  • Architecture & Automation: Microservices architecture, Python, Bash scripting, YAML configurations.
  • Strong problem-solving and troubleshooting abilities.
  • Ability to work in cross-functional AI/ML and DevOps teams.
  • Good communication skills to explain complex workflows to both technical and non-technical stakeholders.
  • Time management and prioritization skills in a fast-paced development environment.

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