Assistant General Manager, AI Engineering

m1 limited; Singapore; On-site; SGD 230,000 - 360,000; Full time

Job summary

m1 limited in Singapore seeks an Assistant General Manager, AI Engineering to lead and scale a forward-deployed AI engineering function. You will partner with business teams to deliver measurable outcomes through production-grade AI services and robust architectures.

You will shape the AI platform, drive reliability, and mentor a high-performing engineering team, coordinating with security, data, and platform functions to ensure successful end-to-end delivery.

Qualifications

• Bachelor's degree in CS/Engineering/Data Science/AI or related field.
• 10+ years technology/software engineering experience with AI/ML delivery.
• 5+ years delivering production AI/ML applications, incl. LLM-based work.
• Proven ability to translate business problems into measurable outcomes.

Responsibilities

• Lead forward-deployed AI engineering initiatives with business units.
• Architect and deliver production-grade AI services, APIs and workflows.
• Design retrieval, embeddings, context assembly and tool execution.
• Build runtimes for models: contracts, state, retries and safety gates.
• Ensure release readiness, monitoring, and incident diagnosis with platform teams.
• Oversee data pipelines, quality, schemas and lineage.
• Set coding standards and collaborate with cyber security and auth teams.
• Lead and develop an AI engineering team with clear goals and growth.

Skills

10+ years of technology/software

5+ years AI/ML delivery

LLM engineering

Python

SQL

APIs & integrations

Cloud deployment

PyTorch

MLOps

Model evaluation & monitoring

Team leadership

Security & access control

Education

Bachelor's in CS/Engineering/Data Science/AI

Tools

Azure OpenAI

OpenAI

Anthropic

AWS / Google AI

MCP

Git / CI/CD

Job description

Role Summary

We are looking for an Assistant General Manager, AI Engineering to lead the forward-deployed AI engineering function, partnering directly with business teams to identify, build and scale production-grade AI solutions that deliver measurable business outcomes.

Reporting to the Head of Data & AI, the role is accountable for end-to-end delivery, engineering standards, technical architecture, production reliability, and the development of a high-performing AI engineering team.

Job Responsibilities:

Forward deployed delivery

• Partner with business stakeholders to understand workflows, test assumptions and define measurable outcomes.
• Lead solutions from discovery and prototype to production, user adoption and support; turn reusable components into shared capabilities.

AI applications and integration

• Lead the development of services, APIs and user-facing workflows using LLMs, retrieval and tool execution.
• Integrate enterprise applications through authenticated APIs and MCP where appropriate, selecting deterministic workflows, agents or conventional ML based on the problem.

Context engineering

• Design retrieval, chunking, embeddings, reranking and context assembly.
• Manage instructions, tool definitions, conversation state, memory, freshness and token budgets; preserve source provenance and enforce access permissions.

Agent harness engineering

• Build the runtime around models: tool contracts, orchestration, state persistence, checkpoints, bounded execution, retries, timeouts and recovery.
• Prevent duplicate side effects and enforce approval boundaries for consequential actions.

Evaluation and production operations

• Establish representative evaluation datasets and regression gates for model, prompt, retrieval and tool changes.
• Trace failures and monitor task success, groundedness, latency and cost per completed task.
• Lead release readiness, rollback and incident diagnosis with platform teams.

Data and model foundations

• Build reliable ingestion and transformation pipelines with Data Engineering.
• Maintain data quality, schemas and lineage.
• Apply deep learning or conventional ML where appropriate, with robust validation and monitoring.

Technical leadership and controls

• Set architecture and code quality standards; prioritise delivery with business owners.
• Work with Cyber Security to implement identity, least privilege, secrets management, auditability and prompt-injection defences.
• Ensure coding agents are used with appropriate testing, review and accountable release decisions.

People management

• Lead and develop a team of AI engineers through clear goals, work allocation, regular feedback and performance reviews.
• Coach technical and career development, address delivery blockers and foster strong team ownership.

Job Requirements:

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Data Science, Artificial Intelligence, or a related discipline.
• 10+ years of technology/software engineering experience, including 5+ years of hands-on experience delivering AI/ML solutions, including production deployment of LLM-based applications or agents.
• Strong expertise in LLM engineering, including RAG, embeddings, structured outputs, tool calling and context engineering.
• Strong proficiency in Python and SQL, with experience in APIs, integrations, automated testing, Git, CI/CD, containers, authentication and production debugging.
• Experience with AI evaluation, failure analysis, reliability monitoring, data pipelines, databases, data quality and access-controlled retrieval.
• Experience with cloud deployment, relational and analytical data stores, model APIs, and delivery pipelines, with demonstrated ability to make sound architecture, technology, and operational decisions. Familiarity with equivalent technologies and frameworks is welcome.
• Strong understanding of machine learning and deep learning fundamentals, including transformers, embeddings, model validation and selecting between prompting, retrieval, fine-tuning and conventional ML.
• Proven ability to translate business problems into measurable outcomes, lead technical architecture and delivery, establish engineering standards, and manage delivery across business, technology and security teams.
• Experience working with business users and product owners, and collaborating with data engineering, infrastructure, DevOps, security, architecture, and application teams to deliver and operate integrated solutions.
• Proven track record in leading and developing AI engineering teams, including setting strategic goals, managing resources, developing talent, coaching engineers, and driving performance.
• Experience with PyTorch, open-weight models, MLOps, multimodal/document AI, MCP or major AI platforms such as Azure OpenAI, OpenAI, Anthropic, AWS or Google AI is advantageous.