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
- 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