Fuku

Machine Learning Engineer

Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia · Work style not stated · Full-time

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About the role

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EmployerFuku
LocationKuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia
Work styleWork style not stated
Employment typeFull-time
Employer posted19 Jun 2026

Position Overview

We are seeking a skilled Machine Learning Engineer to join our team and drive the development, deployment, and optimization of machine learning models and systems. The ideal candidate will bridge the gap between data science research and production-ready ML solutions, working collaboratively to solve complex business problems through innovative ML applications across cloud and edge environments.

Key Responsibilities

Model Development & Implementation
- Design, develop, and implement machine learning models and algorithms to solve business problems.
- Optimize model performance, accuracy, and efficiency through feature engineering and hyperparameter tuning.
- Research and evaluate new ML techniques, frameworks, and tools.

Production Systems & Infrastructure
- Deploy ML models into production environments using cloud platforms and containerization technologies.
- Build and maintain scalable ML pipelines for data processing, model training, and inference.
- Implement MLOps practices including model versioning, monitoring, and automated retraining.
- Ensure model reliability, scalability, and performance in production systems.
- Optimize models for deployment across different environments (cloud, edge).

Show full description

Edge Deployment
- Deploy and optimize ML models for edge devices.
- Implement model compression techniques (quantization, pruning, distillation).
- Work with edge computing frameworks and optimize for resource-constrained environments.
- Ensure low-latency inference and efficient resource utilization.

Data Engineering & Analysis
- Work with large-scale datasets to extract, transform, and prepare data for ML applications.
- Design efficient data pipelines.
- Ensure data quality and implement data validation processes.

Collaboration & Communication
- Partner with data scientists, software engineers, and product teams to define requirements.
- Communicate technical findings and model performance to both technical and non-technical stakeholders.
- Participate in code reviews and maintain high standards for code quality.
- Document models, processes, and best practices.

Required Qualifications

Education & Experience
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related field.
- 3+ years of experience in machine learning engineering or related roles.
- Proven track record of deploying ML models in production environments.

Core Technical Skills
- Proficiency in Python, with experience in ML libraries and frameworks.
- Strong understanding of machine learning algorithms, statistics, and model evaluation techniques.
- Experience with cloud platforms (AWS) and their ML services.
- Knowledge of containerization (Docker, Kubernetes) and orchestration tools.
- Familiarity with ML workflow tools (MLflow, Kubeflow, Airflow).
- Experience with SQL and database technologies.
- Understanding of software engineering principles and version control (Git).
- Familiarity with DevOps practices and CI/CD pipelines.

Edge & Mobile Deployment Skills
- Experience with model optimization techniques (quantization, pruning, knowledge distillation).
- Knowledge of edge computing frameworks (TensorFlow Lite, ONNX Runtime, OpenVINO, TensorRT).
- Understanding of hardware acceleration (GPU, NPU, ARM processors).
- Experience with embedded systems and IoT device constraints.
- Knowledge of model serving on resource-constrained environments.

Soft Skills
- Strong analytical and problem-solving abilities.
- Excellent communication and collaboration skills.
- Ability to work in fast-paced, agile environments.
- Detail-oriented with strong project management skills.

AI Engineering Skills (Good-to-Have)
- Experience with large language models APIs and AI agent frameworks.
- Understanding of prompt engineering, few-shot learning, and in-context learning.
- Familiarity with RAG architectures and semantic search systems.
- Experience building AI agents.
- Understanding of AI safety, alignment, and responsible AI practices.

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