AI Revenue Growth Manager

Key Responsibilities

• Identify, qualify and convert new business opportunities with named frontier AI labs, physical AI and robotics customers, and enterprise AI buyers across China, building and managing a robust pipeline aligned to ambitious revenue targets.

• Develop and execute the regional go-to-market strategy for Keywords AI Data Services, including market mapping, account prioritisation, ICP refinement and proactive outbound prospecting through networking, research, referrals and digital channels.

• Lead consultative, solution-based sales engagements end-to-end: diagnose client problems, position Keywords’ AI data services portfolio (data annotation, model evaluation, red-teaming, embodied AI data) and articulate quantifiable ROI to technical, commercial and C-suite stakeholders.

• Own complex deal cycles end-to-end, from first contact through proposal, negotiation, contracting and successful handover to delivery teams in Singapore, India and across the global studio network.

• Build and nurture long-term, trust-based relationships with clients, partners and industry influencers — positioning Keywords as a strategic advisor rather than a transactional vendor.

• Develop client-centric proposals and commercial models that clearly demonstrate Keywords’ unique value proposition versus pure-play data labelling competitors and in-house alternatives.

• Provide structured market intelligence and competitive insight back into the AI Data Services leadership team to inform product, pricing, packaging and positioning decisions.

• Forecast accurately and maintain disciplined CRM hygiene, taking full ownership of pipeline integrity, conversion metrics and quarterly revenue outcomes.

• Collaborate cross-functionally with delivery, technical leadership (Singapore-based), marketing and operations teams to ensure proposed solutions are feasible and that engagements are structured for long-term account growth.

Requirements

Education & Experience

• Bachelor’s degree in Business Administration, Marketing, Economics, Engineering, Computer Science or a related discipline; an MBA or relevant advanced certifications (sales methodologies, negotiation, AI/ML literacy) is advantageous.
• 8–12 years of progressive experience in enterprise sales, business development or strategic account management within a solutions-oriented B2B environment, with a demonstrable track record of consistently exceeding revenue targets.
• Direct experience selling into AI labs, hyperscalers, foundation model companies, robotics platforms or large technology buyers is strongly preferred, with an established network of decision-makers in at least one of these segments.
• Proven success in launching new service offerings, opening new market segments or building a regional book of business from a low base — comfort with ambiguity and white-space selling is essential.
• Working knowledge of the AI services landscape (data annotation, RLHF, evaluation, red-teaming, synthetic data, embodied AI data pipelines) and fluency with frontier model providers, agentic AI and the application of LLMs in enterprise contexts.

Benefits

Base salary, Bonus, Social insurance and housing funds, Medical check-up, EAP Program

Bachelor’s degree in Business Administration, Marketing, Economics, Engineering, Computer Science or a related discipline; an MBA or relevant advanced certifications (sales methodologies, negotiation, AI/ML literacy) is advantageous. 8–12 years of progressive experience in enterprise sales, business development or strategic account management within a solutions-oriented B2B environment, with a demonstrable track record of consistently exceeding revenue targets. Direct experience selling into AI labs, hyperscalers, foundation model companies, robotics platforms or large technology buyers is strongly preferred, with an established network of decision-makers in at least one of these segments. Proven success in launching new service offerings, opening new market segments or building a regional book of business from a low base — comfort with ambiguity and white-space selling is essential. Working knowledge of the AI services landscape (data annotation, RLHF, evaluation, red-teaming, synthetic data, embodied AI data pipelines) and fluency with frontier model providers, agentic AI and the application of LLMs in enterprise contexts.