AI Engineer Manager

About Us

Metis Data Holding Pte. Ltd. is a Singapore-based AIGC company focused on turning generative AI technology into consumer-facing creative products. We currently operate two core product lines: AI game creation and AI video generation, dramatically lowering the barrier to producing games and video content so that more creators can turn ideas into finished work. Generative AI is the core engine of our business — the AI engineering team directly determines the ceiling of our product experience and the speed of our iteration.

The Role

We are looking for an experienced AI Engineering Manager to take full ownership of the AI engineering team — both its people and its technical quality. You will build and lead the team: hiring, developing, and motivating engineers while ensuring reliable delivery. You will also be the technical gatekeeper: reviewing key technical designs, upholding engineering standards, and identifying technical risks. We are looking for an engineering leader who is technically strong, an effective people manager, and accountable for both delivery outcomes and technical quality.

What You Will Own

People & Team

• Recruit, onboard, and retain AI/ML engineers; own the hiring pipeline, define the technical hiring bar, and participate in technical interviews for key roles

• Run regular 1:1s, performance reviews, and career development plans; build a culture of feedback and growth

• Design team structure, levels, and progression paths as the team scales

Technical Oversight

• Review key technical proposals and architecture designs for feasibility, scalability, and cost-effectiveness

• Define and maintain the team’s engineering quality standards (code review norms, testing requirements, documentation, release processes)

• Identify and manage technical risk and technical debt; make sound trade-offs between delivery timelines and technical quality

• Track the evolution of the AIGC landscape (LLMs, diffusion models and video generation, game AI, model deployment, inference optimization) and judge when and whether to adopt new technologies

• Stay deeply engaged on critical technical problems — able to roll up your sleeves and contribute to solution design and troubleshooting when needed

Delivery & Process

• Own sprint planning, prioritization, and delivery commitments; be accountable for the team’s delivery outcomes

• Establish and continuously improve engineering processes (planning cadence, incident response, on-call, release management)

• Track and report team health, delivery metrics, and system quality metrics to leadership

Stakeholder & Resource Management

• Act as the primary interface between the AI team and other functions (product, sales, leadership); manage expectations and shield the team from churn

• Own the team’s budget, tooling, and vendor relationships (cloud, GPU/compute, ML platforms)

• Translate business priorities into a clear, executable technical roadmap for the team

What We Are Looking For

Must have

• 8+ years in software/ML engineering, including 3+ years managing engineering teams (hiring, performance management, and delivery ownership)

• Hands-on experience building and operating ML/AI production systems, with solid technical depth: able to independently review architecture designs, read critical code, and make well-grounded technology choices

• A systematic understanding of AI/ML engineering (model training and deployment, data pipelines, inference services, evaluation systems), with practical exposure to LLM or generative model applications (video/image generation)

• Strong stakeholder management and communication skills; comfortable presenting to executives and saying no with data

• Experience establishing or improving engineering processes and quality standards in small-to-mid-sized teams

Nice to have

• Experience scaling a team from <10 to 20+ engineers

• Zero-to-one delivery experience with AIGC/GenAI products (video generation, AI games, evaluation pipelines, inference cost management, model deployment)

• Familiarity with the Singapore / APAC tech hiring market

• Still hands-on today — able to contribute to prototyping or critical code reviews when necessary

What Success Looks Like (First 12 Months)

• The team’s hiring plan is executed with a healthy pipeline and a consistently applied technical bar

• Delivery predictability improves measurably (e.g., sprint commitments met, fewer unplanned escalations)

• Engineering quality metrics improve visibly (e.g., lower production incident rate, technical debt paid down on a planned basis)

• Engineers report clear career paths, and the team maintains high retention and morale

Compensation & Benefits

To be discussed during the interview process.