Technical Services Intern

• Research, trial and compare AI coding assistants and developer tools, and recommend what the team should adopt.

• Research, trial and compare AI coding assistants and developer tools, and recommend what the team should adopt.
• Build prototypes and proofs of concept using LLM APIs, prompt engineering, retrieval (RAG) and simple AI agents.
• Help create internal AI tools, such as automated code review, test generation, log analysis or knowledge-base search.
• Write reusable prompts, templates and how-to guides, and run short demos to help colleagues adopt AI.
• Track and report the time saved and quality gained from using AI on real work.

• Support the design, development and testing of applications, integrations and automation scripts.

• Support the design, development and testing of applications, integrations and automation scripts.
• Assist in troubleshooting, debugging and maintaining existing code.
• Work with Git, code reviews and CI/CD pipelines as part of a team.
• Write unit tests and clear technical documentation.

• Apply secure coding basics (OWASP Top 10, input validation, secrets handling).
• Help define safe-use guidelines for AI tools, including data privacy and review of AI-generated code.
• Assist with dependency and vulnerability scanning.

• Recent Degree or Diploma in Computer Science, Software Engineering, Data Science or a related field.

• Recent Degree or Diploma in Computer Science, Software Engineering, Data Science or a related field.
• Programming skills in at least one language (Python, Java, C# or JavaScript/TypeScript).
• Regular, practical use of AI tools (e.g. ChatGPT, Claude, GitHub Copilot, Cursor) in study or personal projects.
• Understanding of core concepts: APIs, databases/SQL and version control.
• Curiosity, a problem-solving mindset, and confidence sharing ideas with others.
• Good written and verbal communication in English.

• A project using LLM APIs, RAG, agents or machine learning (final-year project, hackathon or GitHub portfolio).

• A project using LLM APIs, RAG, agents or machine learning (final-year project, hackathon or GitHub portfolio).
• Coursework or interest in AI ethics, application security or cloud platforms (Azure/AWS).
• Experience with Docker, automation, or low-code/AI workflow tools.

• Mentorship from experienced developers and architects, plus structured product training.

• Mentorship from experienced developers and architects, plus structured product training.
• A visible role shaping how a global team adopts AI in software delivery.
• Real-world experience on customer projects in the marine and terminal operations industry.
• A supportive environment to grow a career in AI-enabled software development.