AI Engineer

Data requirements gathering and client engagement.

• Being able to engage with the client to understand their pain points and requirements.
• Communicate solutions/propositions effectively back to client/team

Data analysis and pre-processing

• Collect and clean large datasets for machine learning projects.
• Explore data to identify patterns, anomalies, and potential insights.
• Pre-process data, including feature engineering and normalization.

Algorithm Development

• Design, develop, and implement machine learning algorithms.
• Experiment with various machine learning models and techniques.
• Optimize algorithms for accuracy, efficiency, and scalability.

Model Training and Evaluation

• Train machine learning models using collected data.
• Perform testing to validate models
• Evaluate model performance using appropriate metrics and techniques.
• Fine-tune models to improve predictive accuracy.

Data Visualization

• Create informative data visualizations to communicate insights effectively
• Familiarity with visualisation libraries like Matplotlib, Seaborn, or data visualisation software’s like Tableau, PowerBI.