AI/ML and Data Science Developer

Key responsibilities:

• Design, develop, and deploy AI/ML models and solutions, including LLMs and GenAI.
• Perform feature engineering and selection to optimize model performance.
• Select and implement appropriate AI/ML algorithms, including supervised, unsupervised, and reinforcement learning models.
• Deploy models to production environments, ensuring robustness and scalability.
• Monitor model performance and define strategies for identifying drift; retrain or refine models as needed.
• Collaborate with cross-functional teams to integrate AI/ML models with business applications and systems.
• Train, evaluate, and optimize models using machine learning and statistical techniques.
• Conduct extensive data exploration, analysis, and preprocessing to ensure data quality for AI/ML applications.
• Develop and apply data science methodologies to extract insights from large-scale structured and unstructured datasets.
• Utilise predictive analytics, time series forecasting, and statistical models to drive business decision-making.
• Stay updated on the latest advancements in AI/ML and data science technologies.
• Develop and maintain comprehensive documentation for AI/ML pipelines, data workflows, and analytical processes

Your Profile

Essential skills/knowledge/experience:

• Good experience in AI/ML development and data science.
• Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems.
• Experience with machine learning frameworks (eg, TensorFlow, PyTorch) and data science libraries (eg, NumPy, pandas, scikit-learn).
• Proficiency in Python, R, or other relevant programming languages.
• Proficiency in working with large datasets, data wrangling, and data preprocessing.
• Experience in data science, statistical modelling, and data analytics techniques.
• Experience with data analysis and visualization tools (eg, Matplotlib, Seaborn, Tableau).
• Ability to work independently and lead projects from inception to deployment.
• Experience with big data technologies (eg, Hadoop, Spark) and cloud platforms (eg, AWS, GCP, Azure) is desirable.