Description : Position Details :
Position Title : Product Owner - AI Centre of Excellence (CoE)
Reporting To : Head - AI CoE
Location : Any
Industry : Telecom & Cloud Services
Qualifications :
- Masters Bachelors in Computer Science, Data Science, or related field.
- 10+ years of experience in data, analytics, or AI leadership roles.
- 3+ yrs of relevant experience AI experience.
- Proven track record in delivering AI / ML solutions at scale.
- Deep understanding of AI governance, MLOps, and responsible AI practices.
- Strong leadership and stakeholder management skills.
- Excellent communication and change management capabilities.
- Product Owner certification (i.e., CSPO, SAFe POPM) preferred.
- Familiarity with telecom BSS / OSS or cloud platforms is a plus.
Job Summary :
To act as the voice of the customer and business within the AI CoE, defining and prioritizing product requirements, coordinating with cross-functional teams, and ensuring that AI / ML products deliver tangible business value.
The Product Owner plays a crucial role in shaping the roadmap and execution of AI / analytics initiatives in telecom and cloud domains.
Key Responsibilities :
Own the product vision, roadmap, and backlog for assigned AI / ML or analytics products.Gather and refine requirements from business stakeholders, domain SMEs, and users.Collaborate with data scientists, engineers, and UI / UX teams to develop high-quality deliverables.Prioritize features and user stories based on business impact, value, and dependencies.Conduct sprint planning, backlog grooming, and user acceptance testing (UAT).Drive continuous feedback loops with users to refine and enhance the product.Ensure alignment with AI CoE governance, data privacy, model explainability, and operationalization standards.Prepare product demos, training, and documentation for effective rollout and adoption.Track KPIs such as accuracy, adoption, ROI, and user satisfaction for AI solutions.Objectives :
Accelerate AI-driven transformation and innovation.Maximize ROI from AI investments through strategic alignment and execution.Promote widespread AI adoption across business units.Ensure responsible, explainable, and secure AI usage.Key Result Areas (KRAs) :
Number and impact of AI use cases deployed.AI adoption rate and cross-functional engagement.Accuracy, reliability, and relevance of deployed models.Compliance with AI governance and ethical standards.Training hours and AI upskilling metrics across the organization.Expected Outcomes :
Scalable and reusable AI solutions across the enterprise.Improved decision-making and operational efficiency.Tangible business advantage through applied AI innovation.Strong AI governance and minimized risk exposure.Key Competencies :
Product Thinking & Business Value OrientationAgile Delivery & Stakeholder ManagementTechnical Curiosity & AI AwarenessCommunication & Storytelling with DataCross-functional Collaboration(ref : hirist.tech)