01Overview
Be part of the Cisco Webex Contact Center team, where technology, innovation, and collaboration come together to transform customer experiences. We foster a culture of curiosity, creativity, and continuous learning, empowering team members to experiment, challenge existing approaches, and bring new ideas to life. As a Data Architect for Webex Contact Center, you will serve as a senior technical leader responsible for defining the architecture, standards, and strategic direction of the data, analytics, and reporting platform. You will design scalable, secure, reliable, and future-ready data architectures that support real-time and historical reporting, customer journey insights, artificial intelligence, machine learning, and other data-driven Contact Center experiences. Working closely with Product Managers, engineering leaders, you will shape how Contact Center data is collected, processed, modeled, stored, governed, and consumed. Your technical direction will help ensure data accuracy, consistency, security, scalability, and high availability while enabling faster delivery of reporting, analytics, and AI-powered capabilities for Webex Contact Center customers. Key Responsibilities Define the AI-native Data Strategy Own the long-term architecture and technical vision for the Webex Contact Center AI driven Data Platform, enabling next-generation AI-powered customer experiences, insights, automation, and decision intelligence. Define the architectural AI layer that transforms enterprise contact center data into intelligent services using custom AI and machine learning models rather than relying solely on foundation models available in the market. Design reusable AI capabilities that continuously learn from customer interactions, operational data, business outcomes, and feedback loops to improve routing, reporting, forecasting, agent assistance, customer journey analysis, workforce optimisation, and conversational intelligence. Architect Large-Scale Data Platforms Define modern cloud-native data architectures spanning streaming, operational analytics, lakehouse technologies, feature engineering, batch processing, real-time processing, and AI-ready data pipelines. Design highly scalable, resilient, secure, and low-latency data platforms capable of processing billions of customer interaction events while supporting enterprise-grade availability and multi-region deployments. Lead architecture for data ingestion, event streaming, storage, metadata management, observability, governance, API access, and AI consumption patterns. Drive architecture decisions around modern data stacks including streaming platforms, distributed processing frameworks, vector databases, analytical databases, feature stores, and AI infrastructure. Drive AI Innovation Partner with Product Management to identify opportunities where custom AI models can create differentiated customer value beyond general-purpose LLM capabilities. Evaluate emerging AI techniques including supervised learning, reinforcement learning, graph learning, recommendation systems, sequence modelling, agentic workflows, and domain-specific foundation models. Lead experimentation around fine-tuning, domain adaptation, model optimisation, inference performance, and enterprise-scale deployment of AI models. Technical Leadership Integrate and add value as part of the Data Architectural team across Webex Contact Center. Drive architectural standards, design reviews, technology selection, and engineering best practices. Mentor architects and senior engineers on AI-first platform design, distributed systems, and modern data engineering. Influence engineering strategy across Product, AI, Platform, Reporting, Analytics, and Customer Experience organisations. Mandatory Skills 15+ years designing distributed cloud-native SaaS platforms, with significant experience as an architect for enterprise-scale products. Deep expertise designing modern data platforms using streaming, lakehouse, distributed storage, real-time processing, and analytical systems. Strong experience architecting AI-native platforms where machine learning models are integral to the product architecture rather than standalone services. Proven experience building or architecting custom machine learning models for enterprise applications, including feature engineering, training pipelines, model evaluation, deployment, monitoring, and continuous improvement. Strong understanding of modern AI architectures including LLMs, Retrieval-Augmented Generation (RAG), vector search, embedding models, semantic retrieval, feature stores, inference optimisation, and model orchestration. Experience optimising AI inference performance, latency, throughput, scalability, and operational cost for large-scale production workloads. Deep understanding of distributed systems, event-driven architecture, microservices, streaming platforms (Kafka, Flink, Pulsar or equivalent), and cloud-native architectures. Strong expertise in data .
