Product Manager (AI) - Hybrid

AI Product Manager Location: Hybrid (Office/Home) Schedule: Dayshift Type: Full-Time Role Overview As an AI Product Manager , you will be the visionary and driving force behind our next generation of conversational AI and voice automation solutions. In this role, you aren’t just managing a product; you are architecting how our customers interact with us. You will bridge the gap between complex AI capabilities (Google Dialogflow CX, NLU, Machine Learning) and real-world business impact, ensuring our virtual assistants are intuitive, scalable, and human-centric. Key Responsibilities Strategic Leadership & Roadmap Vision & Execution: Define and champion the long-term product vision for voice and virtual assistant technologies. Strategic Planning: Translate business goals into a prioritized product roadmap, ensuring every feature adds measurable value to the customer experience. Conversational Design & Technical Ownership End-to-End Delivery: Own the product lifecycle from initial ideation and conversational design to deployment and post-launch optimization. Technical Deep-Dive: Provide hands-on guidance for Dialogflow CX configurations, including intent mapping, entity extraction, fulfillment logic, and complex flow design. Continuous Improvement: Monitor AI performance metrics (recognition rates, containment, and CSAT) to iteratively refine NLU models. Stakeholder & Team Collaboration Cross-Functional Synergy: Lead a multidisciplinary "pod" of AI engineers, data scientists, and QA specialists to deliver high-stakes AI initiatives. Business Partnership: Act as the primary liaison for Operations, Collections, and CRM teams to identify automation opportunities and pain points. Qualifications Experience: Minimum 2+ years of dedicated experience in Conversational AI, Automation, or NLP-driven products . Technical Literacy: A strong grasp of the AI/ML lifecycle, including data labeling, model training, and MLOps. Cloud Proficiency: Comfortable navigating Google Cloud Platform (GCP) ; familiarity with Azure or AWS is a plus. Certification: Azure certifications (AI-900, DP-900) or Google Professional Cloud Security/Data Engineer certifications are highly regarded. Analytical Mindset: Proven ability to define success metrics (KPIs) and use data to back up product decisions. Soft Skills: Exceptional communication skills with the ability to explain complex technical concepts to non-technical stakeholders.