Machine Learning Engineer developing and optimising ML models at Junglee Games. Working on data processing, model productionisation, and collaboration with engineering teams.
About the role
- Machine Learning Engineer developing predictive models and algorithms for Solera. Collaborating with multidisciplinary teams to enhance AI capabilities across international operations.
Responsibilities
- Design, develop, and validate predictive models, regression algorithms, and time‑series forecasting models with a strong focus on performance, accuracy, and robustness.
- Contribute to the full model lifecycle: research, experimentation, industrialization, deployment, and monitoring.
- Build machine learning and deep learning models using Python, Spark MLlib, TensorFlow, and PyTorch.
- Optimize and integrate models into distributed data pipelines running on Cloudera, Spark, and Data-as-a-Service (DaaS) architectures.
- Collaborate with Data Engineers to ensure efficient data ingestion, preparation, and feature engineering in large‑scale environments.
- Work with Data Architects to ensure algorithmic solutions comply with architectural principles, data governance practices, and security standards.
- Partner with Data Scientists to design experiments, evaluate feature sets, and improve model quality.
- Contribute to product‑oriented initiatives by working with Product Managers and occasionally customers to ensure models address real business needs.
- Apply best practices in MLOps, including CI/CD for ML, model monitoring, drift detection, and automated retraining.
- Ensure strict compliance with data privacy, security, and governance policies across all algorithmic developments.
- Stay informed on the latest advances in machine learning, deep learning, and model optimization techniques.
- Participate in agile ceremonies such as sprint planning, architecture reviews, and continuous integration/deployment activities.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Physics, or a related field.
- Demonstrated experience in predictive modelling, regression, and time‑series analysis
- Machine learning and deep learning techniques
- Python and related scientific libraries (NumPy, Pandas, Scikit‑learn)
- TensorFlow or PyTorch
- Spark MLlib for distributed model training
- Deploying models into production environments
- Hands‑on experience or strong motivation to work with on-premise platforms and cloud platforms.
- Curiosity and the ability to rapidly learn new technologies, frameworks, and research methods.
- Strong analytical, problem‑solving, and communication skills.
- Ability to work collaboratively in multidisciplinary, international teams.
- English is a must — you must be able to communicate effectively with global stakeholders.
Benefits
- Health insurance
- Professional development opportunities
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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