Machine Learning Engineer at SSG

Type: Contract

Description

SSG, the National Skills Authority, is seeking a Machine Learning Engineer to join their Data Management and Operations team. The successful candidate will design, build, and deploy scalable machine learning systems, focusing on developing robust data pipelines and ensuring the reliability and efficiency of machine learning solutions. This role involves optimizing ML model performance, maintaining end-to-end data architecture, implementing MLOps and CI/CD processes, and ensuring system reliability and governance compliance.

Required Skills

Data Engineering
Python
SQL
TensorFlow
PyTorch
Scikit-learn
CI/CD
DevOps
Containerization
Large-scale Data Processing

Additional Information

Required Previous Experiences: Proficiency in data engineering practices with demonstrated experience building scalable data pipelines and managing large-scale data processing workflows.
Strong programming skills in Python and SQL with experience in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn for model development and deployment.
Experience deploying machine learning models in production environments with knowledge of CI/CD practices, DevOps methodologies, and containerization technologies.
Hands-on experience working with large and multiple datasets, data warehouses, and cloud-based data platforms with understanding of data governance and quality management principles.
Preferred Qualifications: Strong understanding of ML algorithms, model evaluation techniques, and performance optimization strategies with experience in model monitoring and automated retraining workflows.
Strong analytical, conceptualization, and debugging skills with ability to troubleshoot complex technical issues across the full ML pipeline.
Proven ability to work independently while contributing effectively to cross-functional teams in fast-paced, collaborative environments.
Excellent written and verbal communication skills with demonstrated ability to explain complex technical concepts clearly to non-technical stakeholders including product managers and policy teams.