Trainee, AI & PLM Development

Role Overview

We are looking for an enthusiastic Trainee with a strong interest in Artificial Intelligence to join our team. In this role, you will work alongside experienced engineers and domain experts to design, prototype, and deploy AI-driven solutions that solve real business problems. This is an excellent opportunity to build hands-on experience with Generative AI, LLMs, and modern automation tools in an enterprise environment.

Key Responsibilities

• Assist in developing, testing, and deploying AI/ML and Generative AI solutions to automate and enhance business processes.
• Build and iterate on prompts, AI agents, and copilots using tools like Claude, GPT, LangChain, or the Claude Agent SDK.
• Support data collection, cleaning, labelling, and analysis to feed AI models and evaluations.
• Prototype RAG (Retrieval-Augmented Generation) pipelines using embeddings and vector databases.
• Contribute to workflow automation by integrating AI with existing enterprise systems (APIs, databases, document repositories).
• Document experiments, benchmarks, and learnings; share knowledge with the team.
• Stay current with the fast-evolving AI landscape and propose relevant new ideas.
• Awareness of ISO 14001 and 45001 standards.

Qualifications

• Bachelor's or Master's degree (or final-year student) in Computer Science, Data Science, AI, Software Engineering, or a related field.
• Solid programming skills in Python (mandatory).
• Working understanding of AI/ML fundamentals: LLMs, prompt engineering, embeddings, fine-tuning basics, evaluation methods.
• Experience calling LLM APIs (Claude, OpenAI, or similar) in personal projects or coursework.
• Familiarity with Git, VS Code, REST APIs, JSON, and modern developer workflows.
• Strong analytical thinking, curiosity, and eagerness to learn.

Soft Skills

• Clear written and spoken English communication.
• Team-oriented, proactive, and able to work independently on ambiguous problems.
• Ability to translate business needs into concrete technical experiments.
• Growth mindset and comfort with rapid iteration.

What You Will Learn

• Practical, production-grade application of Generative AI in an enterprise setting.
• End-to-end AI project lifecycle - from idea to prototype to deployment.
• Working with cross-functional teams (engineering, business, domain experts).
• Enterprise-grade software practices: version control, code review, secure coding, DevOps.
• Responsible AI: safety, evaluations, and human-in-the-loop design.