AI Engineer (Generative AI, LLMs, Agentic AI)

About the role

An AI team is looking for a motivated AI Engineer with 1-2 years of hands-on experience. The engineer will work on real-world AI, Generative AI and Agentic AI projects, designing and developing intelligent solutions for enterprise and government customers. The role suits someone with a solid background in computer science, software development, machine learning and AI who wants deeper hands-on experience with Agentic AI, LLMs, RAG and enterprise AI.

Key responsibilities

• Design and build Agentic AI projects, AI agents, intelligent workflows and multi-agent solutions for real business use cases.
• Develop Generative AI and LLM-powered applications, using RAG, embeddings, vector databases and enterprise data.
• Develop, train, evaluate and optimise machine learning and deep learning models, and work on NLP solutions.
• Build production-quality AI applications and APIs in Python, integrating them with enterprise apps, databases and business systems.
• Work with Azure OpenAI, Microsoft Foundry, Copilot Studio and other Microsoft AI technologies.
• Perform prompt engineering, model evaluation, testing and optimisation, and support deployment, monitoring and continuous improvement of AI models.
• Collaborate with solution architects, developers and business teams, applying security, privacy and Responsible AI principles.

Required profile

• 1-2 years of professional or relevant hands-on experience in AI, machine learning, software development, data science or a related field.
• Bachelor's or Master's degree in Computer Science, AI, Software Engineering, Computer Engineering, Mathematics, Data Science or a related field, or equivalent practical experience.
• Ability to work in a multidisciplinary team and communicate technical concepts clearly.

Required skills

• Strong foundation in computer science and software development, including data structures, algorithms and production-quality coding.
• Understanding of machine learning, neural networks and deep learning; hands-on NLP experience.
• Proficiency in Python; familiarity with PyTorch, TensorFlow or similar frameworks.
• Practical understanding of Generative AI and LLMs, RAG architectures, embeddings, vector search and prompt engineering.
• Familiarity with REST APIs, databases, Git/GitHub and modern software development practices.