Voice AI Lead Architect

Salary

Salary: £125,000 – 125,000 per year

Requirements

• Strong expertise in Voice AI / Conversational AI architecture
• Deep knowledge of Data Architecture, including data lakes, pipelines, streaming, and analytics
• Experience with the GCP data stack, including BigQuery, Pub/Sub, Dataflow, and Cloud Storage
• Understanding of RAG, embeddings, and knowledge retrieval frameworks
• Strong stakeholder engagement and consulting skills
• 12–18 years of architecture experience with a focus on data and AI platforms
• Proven experience in Voice AI, IVR, or 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: experience with Customer 360, real-time personalization, and behavioral analytics
• Preferred: exposure to multi-agent AI architectures and tool invocation frameworks
• Preferred: experience with CCaaS platforms such as Google CES/CXAS, Genesys, NICE, or Amazon Connect
• Preferred: strong understanding of AI/ML lifecycle, MLOps, and data governance
• Preferred: experience working with Tier-1 banks or large financial institutions
• Preferred certifications: Google Cloud Professional Data Engineer, Google Professional Cloud Architect, Google Machine Learning Engineer, Conversational AI certification (Dialogflow CX or equivalent), TOGAF / Enterprise Architecture certifications, and data certifications such as CDMP, Databricks, or Snowflake

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, and Vertex AI
• Define and implement enterprise data architecture for Voice AI, including conversation data pipelines, real-time and batch processing, and integration with data lakes and warehouses such as BigQuery
• Enable Customer 360 and contextual data for Voice AI use cases
• Build RAG-based knowledge systems integrating structured and unstructured banking data
• Architect data-driven decisioning for voice agents, including personalization, next-best action, and fraud detection signals
• Ensure integration with core banking, CRM, and analytics platforms
• Establish data governance, lineage, quality, and compliance frameworks including GDPR and PCI-DSS
• Drive conversation analytics, observability, and feedback loops to continuously improve AI performance

Technologies

• AI
• Architect
• BigQuery
• Cloud
• CRM
• Databricks
• GCP
• Genesys
• Machine Learning
• TOGAF

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