Machine Learning Engineer

Responsibilities

• Develop and optimise predictive ML models using classical algorithms such as XGBoost, Gradient Boosting, Random Forest, and LightGBM.
• Build and maintain end-to-end ML pipelines from data preprocessing, feature engineering, and model training to validation and deployment.
• Apply probabilistic modelling and forecasting techniques (e. g., MCMC, Bayesian models) where relevant.
• Write efficient, reusable, and well-documented Python code for data processing, model integration, and business rule automation.
• Collaborate with stakeholders to translate business requirements into ML solutions.
• Communicate analytical insights through clear visualisations and presentations tailored for non-technical stakeholders.
• Participate in client discussions, requirement gathering, and delivery reviews.
  

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related field.
• 3-8 years of experience in applied ML and predictive modelling.
• Proficiency in Python, NumPy, Pandas, Scikit-learn, and ML libraries like XGBoost or LightGBM.
• Experience in building end-to-end ML pipelines and deploying models in production.
• Solid understanding of data preprocessing, validation, and feature engineering.
• Strong analytical thinking and communication skills for stakeholder interaction.
• Exposure to probabilistic modelling, Bayesian inference, or time-series forecasting.
• Experience with MLOps tools such as MLflow, Airflow, or Docker/Kubernetes.