Full Time Manager, AI Engineering Jobs, in RSM Chio Lim LLP - Maukerja

Roles & Responsibilities

As Manager, AI Engineering, you will lead a small team of AI engineers, remaining substantially hands-on, and be accountable for the quality, reliability and responsible use of the AI solutions the team delivers.

• Team leadership: Lead, coach and develop AI engineers, and set priorities and allocate work. Establish standards for solution design, testing, documentation and code review, and promote responsible use of AI-assisted development tools.
• AI solution delivery: Translate business needs into practical AI solutions, and lead the design, development and deployment of LLM-based agentic workflows, retrieval-augmented generation (RAG) pipelines and machine learning models for audit and firm processes. Manage scope, timelines and trade-offs, and deliver solutions through well-defined APIs that follow the platform's CI/CD standards, for integration into the firm's central platform.
• Evaluation and governance: Define how AI outputs are evaluated and reviewed before they are relied upon, including human-in-the-loop controls. Work with IT and governance team on data security, client-data confidentiality and responsible AI.
• Data pipelines: Build and maintain the data pipelines that AI solutions depend on, keeping outputs traceable to their sources.
• Stakeholder collaboration and support: Work with business users and project sponsors to agree priorities, communicate AI capabilities and limitations, and support adoption. Work with fellow Data Science managers to staff and deliver cross-team projects. Ensure solutions are documented, monitored and supportable after go-live, and agree support handovers with Full-Stack Engineering for AI features within applications.
• Research: Track developments in machine learning, LLMs and agentic frameworks, and bring the most promising into practical use.
  
  

Requirements:

• Degree in Computer Science, Data Science, Statistics, Mathematics or a related field.
• At least 5 years of experience building and deploying machine learning solutions in production, with a strong foundation in machine learning and model evaluation.
• At least 2 years of hands-on experience developing and deploying LLM-based agentic workflows in production.
• Experience in leading engineers or serving as a technical lead.
• Strong working knowledge of Python, SQL, machine learning libraries (e.g. PyTorch, scikit-learn) and LLM and agent frameworks (e.g. LangChain/LangGraph), with sufficient hands-on depth to review designs and contribute when needed.
• Experience with LLM evaluation, vector search and cloud AI services (e.g. Azure OpenAI, Azure AI Search); experience building data pipelines is an advantage.
• Strong communication and stakeholder management skills, with sound judgement on AI risk and governance.
• Experience in professional services, audit or another regulated environment is an advantage.

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