Technical Product Manager

AI Product Manager

Key Responsibilities

Product Strategy & Roadmap

• Own the

  AI product strategy and roadmap

  , ensuring alignment with company-wide goals.
• Identify opportunities to embed

  AI/ML capabilities

  across features, UI/UX, personalization, automation, payments, and customer experience.
• Prioritise initiatives based on

  business impact, user value, and technical feasibility

  .

User & Market Understanding

• Deeply understand

  user behaviour, cultural nuances, and commercial dynamics

  through research, data insights, and market analysis.
• Act as the

  voice of users

  , advocating for their needs and preferences in product development.
• Ensure AI products are built with

  ethical, explainable, and responsible practices

  .

Product Development & Execution

• Collaborate cross-functionally with

  engineering, research, design, and global/regional teams

  to define, build, test, and launch high-impact features.
• Drive the

  end-to-end product lifecycle

  — from ideation to rollout, measuring success, and iterating based on feedback.
• Combine

  big-picture thinking with hands-on execution

  , and step into the details when needed.

Qualifications

Must-Have

• 4-5 years of product management experience

  , including ownership of product strategy and execution in cross-functional settings.
• Proven experience in

  AI products

  , with a strong understanding of

  user behaviour and market dynamics

  .
• Strong understanding of

  AI/ML concepts, data-driven product development, and user engagement strategies

  .
• Excellent communication and leadership skills with the ability to build

  alignment across global teams

  .
• Demonstrated ability to thrive in

  ambiguous environments

  , solve hard problems, and approach decisions with a

  first-principles mindset

  .

Good-to-Have (Technical/Architectural Knowledge)

• Familiarity with

  software/system architecture

  , APIs, and cloud infrastructure.
• Exposure to

  scalable AI systems, MLOps, and distributed computing

  .
• Understanding of

  data engineering, microservices, and DevOps practices

  .