AI Product Manager

Lifecycle Management:

Manage the entire product lifecycle, from ideation and design, to launch and ongoing improvement for industry-facing AI products including LLM products, conversational agents, live-streaming solutions and intelligent editing tools while addressing challenges related to data privacy, ethical considerations, and the continuous evolution of AI technologies.

Product Strategy:

Developing a clear product strategy for AI applications, defining the product requirements and specifications, roadmap and vision, and prioritizing features based on user needs and market trends is crucial for this role.

Collaboration:

Collaborate closely with Design, R&D, Operations and GTM teams to translate business requirements into actionable plans for AI development, ensuring alignment with overall business goals with strong competitive positioning of LLM products in industry scenarios

Identifying Opportunities:

Identify business opportunities where AI can be applied effectively, ensuring that the products developed can effectively deliver real value to users.

Monitoring and Evaluation:

Monitor product performance, gather user feedback, and iterate on the product to enhance its effectiveness and user satisfaction.

Requirements

1. Minimum Degree in Computer Science, Engineering, Electronics,

Mathematics or a related field

2. Minimum 2 years relevant experience with AI products for B2B or

B2C

3. Prior experience in live-streaming or intelligent video/audio editing

projects preferred.

4. Able to work in a multi-national environment overseas experience is

a plus.

5. Knowledge of supervised and unsupervised learning, model

evaluation metrics, and data pipelines.

6. Able to conduct data analysis as there is a need to collaborate on

data labeling tasks and conduct error analysis.

7. Strong communication skills are necessary to act as a bridge

between technical teams and business stakeholders, ensuring

everyone is aligned on product goals.

8. Knowledge of ethical AI practices, including fairness, bias mitigation,

and model transparency