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In office (Hyderabad)
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Senior · 5+ years exp
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ansrsource is a learning design and content development company headquartered in Austin, Texas, and founded in 2003. The company provides end-to-end digital learning solutions, including custom course design, accessibility services, content digitization, and AI-powered learning transformation for educational institutions and corporations. Operating globally with a team of over 200 learning architects, it serves educational publishers, non-profits, and global capability centers through its offices in the United States and India.

Responsibilities:

  • Build and maintain the Agile team backlog with ready user stories to support continuous sprint execution for AI agent development, enterprise AI platform features, and MCP connector integrations.
  • Collaborate with AI Engineering, Data Science, and UX teams to write user stories and define acceptance criteria for AI - powered features, LLM - driven workflows, and agentic system behaviours.
  • Facilitate synchronisation meetings with other product owners, coordinate with offshore scrum teams, and mentor peers to enhance product ownership capabilities across AI initiatives.
  • Review and accept completed user stories to ensure they meet acceptance criteria, AI model performance benchmarks, and quality standards for production AI systems.
  • Support AI Product Management in creating and managing product vision, technical roadmaps, and go - to - market strategies for enterprise AI solutions, including AI assistants, digital avatars, and intelligent automation.
  • Coordinate with other product owners and enterprise system teams to align backlogs for large, cross - functional AI initiatives spanning multiple connectors and business domains (e. g., Sales Assist, Contract Management, IT Service Management).

Requirements:

  • Bachelor's Degree plus 5 years of related work experience or an advanced degree with 3 years of related experience with Agile backlog/project management tools (e. g., Jira, Confluence, GitLab).
  • Familiarity with AI development lifecycle and iterative model improvement workflows.
  • Ability to effectively and efficiently communicate with Leadership, technical and non - technical audiences while employing a high degree of collaboration and influence.
  • Experience communicating AI capabilities, limitations, and governance requirements to diverse stakeholders with successive elaboration and the ability to develop initiatives, features, and User Stories that the AI Engineering and DevOps teams can ingest.
  • Understanding of AI agent architectures, prompt engineering concepts, and enterprise AI integration patterns.
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