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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