Support the design and development of AI solutions, intelligent applications and orchestration workflows for digital transformation projects, from early prototype through pilot, under technical guidance
Develop and test AI-assisted features for scenario analysis, decision support and operator advisory, with clear separation from vital, safety-related functions
Build working prototypes using large language models, retrieval-augmented generation (RAG), APIs and Siemens enterprise AI platforms such as the LLM Gateway
Support integration of AI prototypes with simulation, digital-twin, operational-data and test environments
Implement logging, traceability and explainability features based on defined project requirements
Create test cases, run experiments and evaluate the quality, reliability and limitations of AI outputs using agreed methods and metrics
Follow established software engineering, secure development, testing and version-control practices
Build simple human–AI interface prototypes and demonstrate results to project teams and stakeholders
Contribute to reusable AI components, technical documentation and team knowledge sharing
Collaborate with AI, software, system and safety engineers, as well as global Siemens teams, in an agile working environment
Keep up with developments in applied AI, experiment with relevant technologies and share practical findings with teams
Job requirements:
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related field
Up to 3 years of experience in software, data or AI development. Fresh graduates with relevant academic projects, internships, research or personal portfolios are also welcome
Good programming foundation in Python and familiarity with object-oriented programming, debugging and basic software testing
Basic understanding of large language model applications, APIs, prompt engineering, embeddings and retrieval-augmented generation (RAG)
Practical experience using Git and the ability to write clear, maintainable code and technical documentation
Strong analytical and learning ability, attention to details and willingness to explore fast-moving technologies responsibly
Ability to understand operational or business problems; contribute to practical AI solutions and work effectively in a multidisciplinary team
Proactive, responsible and willing to contribute ideas, seek feedback and see ambiguity as an opportunity to experiment and grow
Good written and spoken English; Cantonese and/or Mandarin is an advantage
Exposure to LLM or agent frameworks such as LangGraph, LangChain, AutoGen or Semantic Kernel; Vector databases, cloud platforms, Docker, model evaluation or monitoring is an advantage
Knowledge of TypeScript or Java, simulation, digital twins, machine learning frameworks, knowledge graphs, blockchain or immutable ledger technologies is also a plus