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 TPBN1_UKTJ
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