Intern - AI Engineering

Role Purpose

Support the Digital & AI Centre of Excellence by helping convert business requirements into practical AI proof-of-concepts and delivery-ready solution components. The role is intended for a fresher or early-career engineer who can learn quickly, use AI-enabled coding platforms responsibly, and work with focus and discipline from experiment to production handoff.

This is a hands-on builder role, not a research-only role. The expectation is to understand the problem, build a working first version, test it with users, document what works and what does not, and support senior engineers in moving successful ideas toward secure, maintainable production use.

Core Expectations

1. Understand requirements and shape practical AI POCs

• Work with business and product teams to understand workflows, pain points, data availability and expected outcomes.
• Break ambiguous requests into clear problem statements, assumptions, success criteria and a first POC approach.
• Build quick prototypes using approved AI tools, APIs, prompts, retrieval patterns, workflow agents or simple interfaces.
• Test early with users and capture feedback, limitations, risks and next steps clearly.

2. Execute with engineering discipline

• Write clean, understandable code with proper use of Source control
• Use AI-assisted and agentic coding platforms productively while reviewing, debugging and owning the final output.
• Create small test sets, compare outputs, identify failure cases and improve prompts, retrieval logic or code iteratively.
• Maintain clear notes on decisions, dependencies, assumptions, known issues and handover requirements.

3. Support movement from POC to production

• Help convert promising POCs into reusable components, APIs, prompts, evaluation checks or workflow nodes.
• Work with senior engineers and technology teams on integration, deployment support, monitoring needs and production handoff.
• Follow approved platform, security, privacy and responsible AI guardrails while handling data and tools.
• Keep solutions simple, modular and maintainable so they can be scaled or reused across business workflows.

Required Experience

• Fresh graduate or candidate with experience in software engineering, AI/ML, data science or related project work.
• Bachelor degree in Computer Science, Engineering, IT, Data Science, AI/ML, Mathematics, Statistics or a related field.
• Academic project, internship, hackathon, GitHub portfolio or capstone work involving AI, automation, data, APIs or application development will be preferred.
• Prior enterprise experience is not mandatory; learning ability, curiosity, discipline and execution focus are valued.

Required Skills

• Strong Python fundamentals (or any coding language of choice), basic debugging, clean coding habits, Git/Bitbucket usage and comfort working with libraries.
• Basic understanding of SQL, data cleaning, APIs, JSON and structured or unstructured data handling.
• Awareness of GenAI concepts such as prompts, embeddings, RAG, structured outputs, tool calling and hallucination risks.
• Comfort using AI-enabled coding and productivity tools responsibly to accelerate development and research.
• Ability to document work clearly enough for review, reuse and handover.
• Natural curiosity, persistence, ownership mindset and ability to stay focused through iterations.

Good to Have

• Exposure to LangChain, LlamaIndex, LangGraph, CrewAI, n8n or similar agent/workflow tools.
• Basic familiarity with vector databases, document processing, cloud services, Docker, CI/CD, logs or monitoring.
• Interest in building productivity tools, automation utilities and user-facing AI assistants that teams actually adopt.

Role Success Measures

• Business requirements are converted into clear POC plans with success criteria and practical delivery steps.
• POCs are built quickly, tested honestly and documented with limitations, risks and recommendations.
• Reusable code, prompts, workflows and evaluation checks are created instead of one-off demos wherever possible.
• Successful POCs move more smoothly toward production handoff with clearer engineering, security and support inputs.
• The engineer shows strong learning velocity, disciplined follow-through and responsible use of AI-enabled tools.