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.