Voice AI Lead Architect Data Architecture - Banking

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 12-18+ 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.