AI & Data Internship:

We are hiring four interns to work alongside the senior AI team on that build. This is not a shadowing programme and not a research placement. You will ship code into a system that around 140 colleagues use to run their week, and a senior engineer will review every line of it.

Must have

Core (this is the 60% — we weight these heaviest):

- Python you are genuinely comfortable in. You can read data from an API or a file,

transform it, and write it somewhere else. Familiarity with pandas and one HTTP client.
- SQL you can write yourself. Joins, GROUP BY, aggregates, and a rough idea of what an index does and why a query might be slow. You do not need to be a query-optimisation expert.
- One of these two, with something to show for it:

o You have built something with an LLM API — a tool-calling agent, a retrieval-augmented app, a classifier prompt, anything real — and you can explain how you knew whether it worked. A course project, a hackathon build, or a side project all count.

o You have trained and evaluated a model properly — a real train/test split,

a metric you chose for a reason, and an honest account of where it failed.

A Kaggle notebook counts if you can defend the choices in it.

Foundation (the minimum floor — we do not expect depth here):

- Basic cloud literacy. You have deployed something to a cloud provider, even a hobby project. You know what a container is and roughly why it exists. You know why credentials do not go in a Git repository.
- Basic data-engineering sense.



You have moved data from one place to another and cleaned it. You understand what a schema is, and you can explain why running the same import twice should not double your rows.

Working habits

- Git, and the ability to read code you did not write without needing it explained line by line.
- Written clarity in English. You can explain a technical decision in a short paragraph a non-engineer would follow. Our proposals and engineering teams —

not just developers — will read what you write.
- Intellectual honesty. When you do not know, you say so; when your result is weaker than you hoped, you report it before someone finds it. This is the single trait we screen hardest for.

Eligibility: current student (undergraduate or postgraduate) in computer science,

software engineering, data science, statistics, or a related field, or a recent graduate within 12 months. Legally able to work in Canada, Malaysia, India or WW locations for the duration of the term.

Nice-to-have (none of these are required)

- Coursework or projects in machine learning, statistics, NLP or information retrieval.
- Exposure to TypeScript, React or Next.js — helpful if you end up on work that touches the product surface.
- Any experience with vector search, embeddings, or a RAG pipeline, however small.
- Docker, Kubernates beyond "I have run one".
- A domain interest in engineering, construction or infrastructure. You do not need it, but if reading about bridge condition assessment or municipal procurement sounds interesting rather than dull, this will be a better term for you.

📌 AI & Data Internship (India)
🏢 Concept Dash
📍 India