全职 Associate, AI Engineering 工作, 薪水, RSM - Singapore 公司招聘中 - Ricebowl

Roles & Responsibilities

As Associate, AI Engineering, you will build, test and support AI solutions as part of a small team. This role suits AI engineers who want hands-on experience delivering LLM and machine learning solutions to real users.

• AI solution development: Develop and test LLM-based agentic workflows, retrieval-augmented generation (RAG) pipelines and machine learning models for audit and firm processes. Deliver solutions through well-defined APIs that follow the platform's CI/CD standards, for integration into the firm's central platform.
• Evaluation and quality: Build evaluation datasets and tests to measure the accuracy and reliability of AI outputs, and support human-in-the-loop review before outputs are relied upon. Follow the team's standards for solution design, testing, documentation and code review, and use AI-assisted development tools responsibly.
• Data pipelines: Build and maintain the data pipelines that AI solutions depend on, keeping outputs traceable to their sources.
• Data security and governance: Handle client data in line with the firm's data security, client-data confidentiality and responsible AI requirements, and raise risks or issues promptly.
• Collaboration and support: Work with business users to understand requirements and gather feedback, and to integrate AI features into applications. Document and monitor solutions after go-live, and help investigate and resolve issues.
• Learning and research: Keep up with developments in machine learning, LLMs and agentic frameworks, and share promising ideas and tools with the team.
  
  

Requirements

• Degree in Computer Science, Data Science, Statistics, Mathematics or a related field.
• Around 1–3 years of experience building machine learning or AI solutions; fresh graduates with relevant internships, academic or personal projects are also welcome to apply.
• Solid foundation in machine learning and model evaluation.
• Working knowledge of Python and SQL, and familiarity with machine learning libraries (e.g. PyTorch, scikit-learn).
• Hands-on experience, through work or projects, building LLM-based applications using LLM and agent frameworks (e.g. LangChain/LangGraph), RAG or vector search.
• Exposure to cloud AI services (e.g. Azure OpenAI, Azure AI Search), APIs, Git and CI/CD pipelines is an advantage.
• Good communication skills, attention to detail, and an appreciation of AI risks such as inaccurate outputs and data privacy.
• Interest in professional services, audit or another regulated environment is an advantage.
  
  

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