The Role
Support AI innovation and software development by researching coding assistants, building LLM, RAG, and agent prototypes, developing automation tools, testing applications, and documenting solutions. Assist with debugging, CI/CD, secure coding, vulnerability scanning, and responsible AI guidelines. The intern will also demonstrate AI tools, measure productivity gains, and collaborate with developers and architects on customer-focused projects.
Summary Generated by Built In
Job Description:
AI initiatives and innovation
- 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.
Software development
- 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.
Secure and responsible AI
- 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.
Qualifications and skills
Required
- 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.
Advantageous
- 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.
- 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.
Kaleris is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Skills Required
- Recent degree or diploma in Computer Science, Software Engineering, Data Science, or a related field
- Programming skills in at least one of Python, Java, C#, or JavaScript/TypeScript
- Regular practical use of AI tools such as ChatGPT, Claude, GitHub Copilot, or Cursor
- Understanding of APIs, databases/SQL, and version control
- Curiosity, problem-solving ability, and confidence sharing ideas
- Good written and verbal communication in English
- Project experience using LLM APIs, RAG, AI agents, or machine learning
- Coursework or interest in AI ethics, application security, or cloud platforms such as Azure or AWS
- Experience with Docker, automation, or low-code/AI workflow tools
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The Company
What We Do
Kaleris is a leading provider of cloud-based supply chain execution and visibility technology solutions. Many of the world's largest brands rely on Kaleris to provide mission-critical technology for yard management, transportation management, maintenance and repair operations, terminal operating systems, and ocean carrier and vessel solutions. By consolidating supply chain execution software assets across major nodes and modes, we address the dark spots and data gaps that cause friction and inefficiency in the global supply chain.
