(Senior) AI Product Manager

Tencent

London

1 year ago

Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer - Research

Data Analyst

Senior Data Scientist

Performance Data Analyst

Level Infinite is a global game publisher offering a comprehensive network of bespoke services for games, development teams and studios around the world.

We are dedicated to delivering engaging and original gaming experiences to a worldwide audience, whenever and wherever they choose to play, while building a community that fosters inclusivity, connection, and accessibility. The brand also provides a wide range of services and resources to our network of developers and partner studios around the world to help them unlock the true potential of their games. Level Infinite is both publisher of breakout hit games like PUBG MOBILE and Goddess of Victory: NIKKE, and a collaborative partner in games such as Stunlock V Rising, Fatshark’s Warhammer 40,000: Darktide, Dune: Awakening from Funcom, Nightingale from Inflexion Studios and many more.

Role Overview:We are looking for a dynamic and experienced (Senior) AI Product Manager to lead and drive innovation within our AI team. This role focuses on defining and executing strategies for AI and AI-powered content generation (AIGC), ensuring the successful development and implementation of cutting-edge AI solutions.

Key Responsibilities:

1.Stakeholder Engagement:Communicate effectively with gaming studios to understand their needs and business requirements. Translate business needs into actionable AI product features and solutions.

2.AI Strategy and Execution:Develop and implement the overall AI strategies and roadmap to align with business objectives and market opportunities. Identify and prioritize AI product opportunities, ensuring alignment with organizational goals.

3.Product Management:Define product roadmaps, ensuring clarity and feasibility in development cycles. Work closely with engineering, data science, and design teams to deliver innovative and impactful AI products.

4.Cross-Functional Collaboration:Work closely with cross-functional teams to ensure a cohesive approach to AI product development and delivery. Act as a thought leader within the organization to promote AI-driven innovation.

Required Qualifications:

1. Bachelor’s degree or higher in Computer Science, Data Science, Business, or a related field.

2. Proven experience in product management, with a strong focus on AI technologies and solutions.

3. Demonstrated ability to define and execute AI strategies and roadmaps.

4. Excellent communication and leadership skills, with the ability to collaborate across diverse teams.

5. Strong ability to engage with stakeholders, particularly gaming studios, to identify and address their needs.

Preferred Qualifications:

1. Experience in the gaming industry or related fields.

2. Familiarity with AI and machine learning frameworks and tools.

3. Proven track record of successfully launching AI products.

4. Strong analytical and problem-solving skills with a data-driven approach to decision-making.

Equal Employment Opportunity at Tencent

As an equal opportunity employer, we firmly believe that diverse voices fuel our innovation and allow us to better serve our users and the community. We foster an environment where every employee of Tencent feels supported and inspired to achieve individual and common goals.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 3, 2026

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Jan 29, 2026

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Jan 24, 2026

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.