Machine Learning Operation Engineer (MLOps Engineer)

Posted 17 Days Ago
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Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur
In-Office
Junior
Events • Marketing Tech
The Role
The MLOps Engineer deploys ML models, manages cloud infrastructure, develops APIs, and ensures efficient ML solutions through collaboration with data scientists and engineers.
Summary Generated by Built In
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.

Top Skills

Airflow
AWS
Azure
C++
CSS
Docker
Elk Stack
Faiss
Fastapi
Flask
GCP
Go
Grafana
Grpc
HTML
JavaScript
Kubeflow
Kubernetes
Lancedb
Litestar
Loki
Microsoft Sql Server
Mlflow
Onnx Runtime
Podman
Postgres
Prometheus
Qdrant
RabbitMQ
Restful Apis
Rust
Zeromq
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The Company
HQ: Kuala Lumpur, Kuala Lumpur
167 Employees
Year Founded: 2000

What We Do

We are Always Marketing, one of the largest Field Marketing service agencies in Malaysia. Our headquarter office based in Shanghai and we have offices in Hong Kong and Taiwan.

We provide a full spectrum of Total Field Marketing Solutions: “Sell In" to “Sell Out", “Activation Strategic Planning”, “On-The-Ground Execution” and Service Offerings, including Promoter & Field Marketer Management, In-Store Activation / Promotion, Retail Audit / Mystery Shopper, Event / Road Show, POSM Management and Premium / Gifting.

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