Voice AI lead Architect

Voice AI Lead Architect (Data Architecture) Banking

Role Summary

We are seeking a seasoned Voice AI Lead Architect with strong Data Architecture expertise to lead the design and implementation of next-generation Voice/Agentic AI solutions for a leading banking client on GCP. This role combines conversational AI data strategy and customer engagement acting as a trusted advisor to drive intelligent data-driven IVR transformation.

Key Responsibilities

• Act as the onsite Voice AI and Data Architecture lead building strong relationships with banking stakeholders across business data and IT teams.
  
• Design and deliver Voice AI / Agentic IVR solutions leveraging:
  
• Google CES/CXAS Dialogflow CX / CCAI
  
• Vertex AI (LLMs RAG agent frameworks)
  
• Define and implement enterprise data architecture for Voice AI:
  
• Conversation data pipelines (real-time batch)
  
• Integration with data lakes warehouses (BigQuery)
  
• Customer 360 and contextual data enablement
  
• Build RAG-based knowledge systems integrating structured and unstructured banking data.
  
• Architect data-driven decisioning for voice agents (personalization next-best action fraud detection signals).
  
• Ensure integration with core banking CRM and analytics platforms.
  
• Establish data governance lineage quality and compliance frameworks (GDPR PCI-DSS).
  
• Drive conversation analytics observability and feedback loops to continuously improve AI performance.
  

Key Skills

• Strong expertise in Voice AI / Conversational AI architecture
  
• Deep knowledge of Data Architecture (data lakes pipelines streaming analytics)
  
• Experience with GCP data stack (BigQuery Pub/Sub Dataflow Cloud Storage)
  
• Understanding of RAG embeddings and knowledge retrieval frameworks
  
• Strong stakeholder engagement and consulting skills
  

Experience

• 1218 years in architecture with focus on data AI platforms
  
• Proven experience in Voice AI / IVR / Contact Center transformation programs
  
• Hands-on experience designing enterprise data platforms in banking
  
• Experience working in regulated financial environments
  
• Track record of driving data-driven CX transformation initiatives
  

Preferred Qualifications

• Experience with Customer 360 real-time personalization and behavioral analytics
  
• Exposure to multi-agent AI architectures and tool invocation frameworks
  
• Experience with CCaaS platforms (Google CES/CXAS Genesys NICE Amazon Connect)
  
• Strong understanding of AI/ML lifecycle MLOps and data governance
  
• Experience working with Tier-1 banks or large financial institutions
  

Certifications

• Google Cloud Professional Data Engineer (Highly Preferred)
  
• Google Professional Cloud Architect
  
• Google Machine Learning Engineer
  
• Certifications in Conversational AI (Dialogflow CX or equivalent)
  
• TOGAF / Enterprise Architecture certifications
  
• Data certifications (good to have): CDMP Databricks Snowflake