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

TPBN1_UKTJ