About the role
AI Product Manager – Conversational AI (Voice & NLU)
Our client, a pioneer in innovative financial solutions, is seeking an AI Product Manager – Conversational AI to lead the strategic development and deployment of voice-based AI solutions. This role serves as an organization-wide leader, owning the vision for automated customer interactions and driving the adoption of sophisticated voice bot technology. The ideal candidate blends technical NLP expertise with product leadership to transform customer experience through automation.
Key Responsibilities
Product Strategy & Roadmap Ownership
Strategic Vision: Define and own the long-term product vision and roadmap for voice-based conversational AI solutions, specifically leveraging Google Dialogflow.
End-to-End Delivery: Lead the full product lifecycle from initial ideation and requirements gathering to deployment, scaling, and continuous optimization.
Success Metrics: Define project scope and success metrics (e.g., containment rates, sentiment accuracy) to track the business impact of AI deployments.
Technical Oversight & Innovation
NLU Configuration: Provide hands-on guidance on Dialogflow CX/NLU configurations, including the design of intents, entities, and complex fulfillment logic.
Technical Guidance: Oversee data processing, model development, and deployment (MLOps) to ensure robust and scalable AI performance.
Cloud Architecture: Navigate cloud-based data architectures to ensure seamless integration of VoiceBOTs into existing ecosystem infrastructures.
Stakeholder & Team Leadership
Cross-Functional Management: Lead a specialized team of AI specialists, data scientists, and QA engineers to deliver high-quality conversational experiences.
Business Alignment: Work closely with Operations, Collections, CRM, and CX teams to translate operational pain points into automated AI workflows.
Advocacy: Act as the internal champion for AI, educating stakeholders on the capabilities and limitations of conversational technologies.
Requirements
Experience & Education
Professional Tenure: Minimum of 3+ years of work experience specifically within the AI field.
AI/ML Foundatons: Solid understanding of Machine Learning, Deep Learning, and Natural Language Processing (NLP) methodologies.
Lifecycle Knowledge: Demonstrated experience in defining project scope, objectives, and technical deployment (MLOps).
Technical Skills & Certifications
Cloud Platforms: Familiarity with major cloud providers (GCP, Azure, or AWS).
Preferred Credentials: Fundamental-level certifications such as Azure AI Fundamentals (AI-900) or GCP equivalents are highly considered.
Tooling: Expertise in Dialogflow CX or similar Conversational AI platforms.
Core Competencies
Leadership: Proven ability to manage cross-functional technical teams in a fast-paced environment.
Communication: Ability to bridge the gap between complex AI technicalities and business-centric outcomes.
Agility: A proactive mindset capable of navigating the rapidly evolving landscape of Generative and Conversational AI.
Originally posted on Himalayas