Tech – Software Engineer: EmPOWER Workplace Fairness

Transform how citizens access government services through cutting-edge conversational AI Master Advanced GenAI Customisation: Learn to fine-tune large language models for specialised government advisory roles, creating natural conversational experiences that feel authentically human rather than robotic, whilst building sophisticated knowledge bases that power contextual, personalised responses. Develop Real-Time Voice AI Systems: Work with cutting-edge GenAI APIs to create seamless voice interactions, enabling citizens to speak naturally about workplace concerns and receive intelligent guidance through advanced natural language processing and speech integration. Build Robust AI Evaluation Frameworks: Gain expertise in performance evaluation methodologies, safety guardrails implementation, and developing custom metrics that measure conversational quality and advisory accuracy in production government systems. Create Deterministic AI Workflows: Use Python to develop sophisticated chatbot workflows that balance AI creativity with reliable, consistent advice delivery, ensuring citizens receive dependable guidance on complex employment issues. Impact Real Citizens' Lives: Work on production-grade government AI that directly transforms how people access workplace justice, whilst mastering the most in-demand technical skills in conversational AI, responsible AI development, and public service technology.

Essential: Python Programming with AI Libraries: Strong Python fundamentals with exposure to machine learning libraries such as scikit-learn, pandas, or numpy through coursework. Basic understanding of working with APIs and JSON data structures for integrating AI services. Large Language Model Understanding: Basic comprehension of how LLMs work, including concepts like training, fine-tuning, and inference. Exposure to popular models (GPT, BERT) or AI platforms through academic projects, online courses, or experimentation with tools like ChatGPT API. Added advantage: Natural Language Processing Basics: Foundational knowledge of text processing concepts, tokenization, and language models through AI/ML modules or personal exploration. Familiarity with concepts like embeddings, prompt engineering, or chatbot frameworks is advantageous. Conversational AI Concepts: Understanding of dialogue systems, intent recognition, and conversation flow design through coursework or personal projects. Knowledge of how chatbots handle context, maintain conversation state, and provide relevant responses. AI Ethics and Evaluation: Awareness of responsible AI principles, bias detection, and model evaluation metrics. Understanding of challenges in AI safety, hallucination detection, and the importance of guardrails in production AI systems serving the public.