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.

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