Associate, AI Engineering

RSM Singapore; Singapore; Presencial; SGD 65.000 - 90.000; Tempo integral

Resumo da vaga

RSM Singapore is seeking an Associate, AI Engineering to build, test and support AI solutions within a small team. You will work on LLM-based workflows, RAG pipelines and ML models for audit and firm processes.

You will develop APIs, evaluate outputs with human-in-the-loop reviews, build data pipelines, ensure data security, and collaborate with business users. Stay updated on AI trends and agentic systems.

Qualificações

• Degree in Computer Science, Data Science, Statistics, Mathematics or a related field.
• 1–3 years of experience building ML/AI solutions; internships or projects welcome.
• Solid foundation in ML and model evaluation.

Responsabilidades

• AI solution development: build and test LLM-based workflows and RAG pipelines with APIs.
• Evaluation and quality: create tests and ensure reliable AI outputs with human-in-the-loop reviews.
• Data pipelines: build and maintain data pipelines with traceable outputs.
• Data security and governance: handle client data per firm policies and responsible AI needs.
• Collaboration and support: work with business users to gather requirements and monitor post-go-live solutions.
• Learning and research: stay updated with ML/LLM developments and share insights.

Conhecimentos

Python

SQL

ML fundamentals

Model evaluation

Communication

Formação

Bachelor's degree in Computer Science / Data Science / Statistics / Mathematics

Ferramentas

PyTorch

scikit-learn

LangChain / LangGraph

Azure OpenAI

Azure AI Search

Git

CI/CD

Descrição da vaga

Our Data Science team develops analytics, artificial intelligence, automation and technology solutions that improve client service delivery and strengthen our professional services offerings.

Your 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.

Your profile

You are a curious, hands-on engineer with a solid foundation in machine learning and a keen interest in LLMs and agentic systems. You learn quickly, take ownership of your work and explain technical concepts clearly to non-technical colleagues.

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