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 (Big Query) 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 (Big Query, 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