Job Summary
As a Machine Learning Engineer, you will be a key contributor in the development, deployment, and optimization of cutting-edge AI solutions. You will work hands-on across the ML lifecycle, from data preprocessing to model training, fine-tuning, and deployment. This role is ideal for someone with a strong foundation in ML who is ready to take ownership of core components within a production pipeline while collaborating closely with senior researchers, software engineers, and product teams.
Key Responsibilities
- Data Preparation & Processing: Manage the acquisition, cleaning, augmentation, and normalization of large datasets for training and evaluation.
- Model Development: Design, train, and fine-tune machine learning and deep learning models (especially using PyTorch), including transformer-based architectures and LLMs.
- Model Evaluation & Optimization: Evaluate models using quantitative metrics and performance benchmarks; apply techniques like hyperparameter tuning and pruning to improve results.
- Pipeline Integration: Integrate models into existing ML pipelines, contributing to system modularity and maintainability.
- Deployment & Monitoring: Support deployment of models in production environments using containerization tools such as Docker and orchestration frameworks like Kubernetes.
- Documentation: Maintain clear and thorough documentation of experiments, methodologies, and versioning across models and datasets.
- Cross-Team Collaboration: Work closely with data engineers, ML researchers, software developers, and product teams to deliver scalable ML solutions that align with business goals.
- MLOps & Workflow Automation: Apply MLOps best practices for reproducibility, continuous integration (CI), and model version control using tools like Git, DVC, and MLflow.
- Research & Innovation: Stay updated on emerging ML technologies, frameworks, and research, and propose solutions to improve existing systems.
General Qualifications
- Bachelor's degree or higher in Artificial Intelligence, Computer Science, Data Science, or a related field.
- 2–5 years of hands-on experience in machine learning engineering or applied AI development.
Experience and Skills
Technical Skills:
- Proficient in Python and deep learning frameworks such as PyTorch.
- Solid understanding of ML algorithms, neural networks, and NLP techniques.
- Experience working with structured and unstructured datasets (text, speech, etc.).
- Familiarity with LLM fine-tuning, transfer learning, and model optimization.
- Experience with version control (Git), Jupyter, and experiment tracking tools.
- Basic understanding of containerization (Docker) and CI/CD pipelines.
- Exposure to cloud platforms like AWS or GCP for training or deployment.
- Familiarity with MLOps tools such as MLflow, DVC, or Weights & Biases.
Soft Skills:
- Strong problem-solving abilities and a proactive mindset.
- Effective communication skills, including ability to explain technical details to non-technical stakeholders.
- Comfortable working in a fast-paced, collaborative environment.
Preferred Experience
- Experience in speech and language processing (e.g., STT, TTS).
- Exposure to real-time ML deployment or streaming data processing.
- Knowledge of Malaysian or Arabic language datasets is a plus.
- Prior work on deploying models in production environments or ML-enabled products.
Work Environment
Office-based with flexibility for hybrid or remote work, depending on company policy.
Reporting Line
This role reports directly to the ML Research Lead.
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