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Job Purpose:

• Support the delivery of AI solutions by contributing to well-scoped modeling, evaluation, and data tasks under mentorship
• Learn and apply the team's existing patterns, tools, and frameworks rather than creating new ones
• Gain hands-on exposure to the end-to-end AI lifecycle (data → model → deployment) in a production environment
• Grow into an independent contributor: ask good questions, escalate early, and turn feedback into progress

Qualifications:

• Final-year student or recent graduate in Computer Science, AI, Data Science, Software Engineering, IT, or a related quantitative field
• Solid foundation in Software Engineering and Core Machine Learning principles
• Available for a minimum commitment of 3–6 months, ideally 4+ days per week

Experience & Skills:

• 3+ months of experience in Software Engineering or AI/ Data engineering is a plus
• Coursework, personal projects, competitions (Kaggle/hackathons), open-source contributions, or research all count

• Exposure to LLM applications, prompting, RAG, or agents, through projects, tutorials, or personal experimentation
• Understanding of the basics of model evaluation: train/test splits, overfitting, common metrics, and why a good benchmark matters
• Awareness of Responsible AI concepts (fairness, transparency, data privacy) and willingness to learn regulatory context in banking
• Bonus: Docker, cloud platforms, Airflow/Kafka, vector databases, or MLOps tooling