Machine Learning Operation Engineer

2 days ago

Kuala Lumpur, Kuala Lumpur, Malaysia Always Marketing Malaysia Full time 80,000 - 120,000 per year

Duties and Responsibilities:
  • Provides deep technical expertise in the aspects of cloud infrastructure design and API development for the business environments.
  • Bridges the gap between data scientists and software engineers, enabling the efficient and reliable delivery of ML - powered solutions
  • Ensures solutions are well designed with maintainability/ease of integration and testing across multiple platforms.
  • Possess strong proficiency in development and testing practices common to the industry
Summary of Principal Job Responsibility & Specific Job Duties and Responsibilities:
  • Working closely with data scientists, ML engineers, and other stakeholders to deploy ML models
  • Setting up and maintaining cloud and edge infrastructure for MIL models deployment
  • Design, implement and maintain scalable infrastructure for ML workloads
  • Good verbal and written communication skills
  • Collaborative and oriented
Academic Qualification (s):
Bachelor's degree in computer science, Engineering or related subject and/or equivalent formal training or work experience

Work Experience / Skills Requirement(s):
1. Cloud Infrastructure & Kubernetes
  • Minimum 2 years of hands-on experience managing cloud infrastructure (e.g. AWS,GCP,Azure) in a production environment
  • Hands-on experience with Kubernetes for container orchestration, scaling and deployment of ML services
  • Familiar with Helm charts, ConfigMaps, Secret and autoscaling strategies
2. API Development & Messaging Integration
  • Proficient in building and maintaining RESTful or gRPC APIs for ML inference and data services
  • Experience in message queue integration such as RabbitMQ or ZeroMQ for asyncronous communication, job queuing or real-time model inference pipelines
3. System Design, Database & Software Architecture
  • Proven experience working with relational databases (RDBMS) such as Microsoft SQL Server and PostgreSQL.
  • Proficient in schema design, writing complex queries, stored procedures, indexing strategies, and query optimization.
  • Hands-on experience with vector search and embedding-based retrieval systems.
  • Practical knowledge using FAISS, LanceDB, or Qdrant for building similarity search or semantic search pipelines.
  • Understanding of vector indexing strategies (e.g., HNSW, IVF), embedding dimensionality management, and integration with model inference pipelines.
4. Programming Languages
  • Demonstrated expertise in building scalable and maintainable API services using Python frameworks such as Flask, FastAPI, or Litestar.
  • Fluent in HTML, CSS, and JavaScript for building simple web-based dashboards and monitoring interfaces.
  • Experience with Go, C++, or Rust is a strong plus, especially for performance-critical or low-latency inference applications.
5. Edge AI Deployment
  • Experience in integrating models using NCNN, MNN, or ONNX Runtime Mobile on mobile and edge devices.
  • Familiarity with quantization, model optimization, and mobile inference profiling tools.
6. MLOps & Tooling
  • Experience with Docker/Podman, CI/CD pipelines, Git, and ML lifecycle tools such as MLflow, Airflow, or Kubeflow.
  • Exposure to model versioning, A/B testing, and automated re-training workflows.
7. Monitoring & Logging
  • Ability to set up monitoring (e.g., Prometheus, Grafana) and logging (e.g., ELK stack, Loki) to track model performance and system health.
8. Soft Skills & Collaboration
  • Strong analytical and troubleshooting skills.
  • Able to work closely with data scientists, backend engineers, and DevOps to deploy and maintain reliable ML systems.
  • Excellent communication and documentation habits.

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