Plan and prioritize product development and product feature backlog
Develop detailed product feature specifications and ensure they’re clearly understood by relevant teams
Assess value, develop cases, and prioritize stories, epics, and themes to ensure work aligns with product strategy
Mitigate roadblocks to achieving sprint/release goals
Lead the product-release plans and set expectations for delivery of new functionalities
Experience:10+ years of experience in Product Management or Product Ownership, with demonstrated ownership of complex product domains or platform services.
Communication: Exceptional verbal and written communication skills in English, with the ability to distill complex technical concepts for non-technical executive stakeholders.
Influence: Advanced negotiation and stakeholder management skills, specifically within large, matrixed organizations involving both billable client work and internal platform scaling.
Frameworks: Mastery of Agile methodologies (Scrum/Kanban); experience with Scaled Agile Framework (SAFe) is highly preferred.
Platform Mastery: Extensive experience managing "Platform-as-a-Product" (PaaP) or complex back-end service domains.
Domain Knowledge: Experience working with enterprise platforms such as eCommerce, CRM, financial/billing systems, or similar complex business platforms.
AI Proficiency: Hands-on experience with AI productivity and development tools (e.g., ChatGPT or Claude for requirements drafting and product analysis, GitHub Copilot or similar tools for technical collaboration, or Jira-integrated AI for backlog insights). Familiarity with modern AI platform capabilities such as LLM-powered services, semantic search or knowledge retrieval systems, and AI-driven data analysis.
Technical Background: Prior experience as a Principal Software Engineer or Technical Architect.
Technical Literacy: A strong conceptual understanding of RESTful APIs, event-driven architectures and messaging platforms (Kafka/RabbitMQ), SQL/NoSQL databases, cloud infrastructure (e.g., AWS), and observability (monitoring/logging). Familiarity with data structures, data pipelines, and modern AI system patterns such as vector embeddings, semantic search, and Retrieval-Augmented Generation (RAG), including concepts like document chunking and knowledge indexing used in LLM-powered applications.
