Cloud Engineer for Webex Contact Center

Be part of the Cisco Webex Contact Center team, where technology, innovation, and collaboration come together to transform customer experiences.

As a Cloud Engineer for Webex Contact Center, you will serve as a Senior Engineer responsible for defining the architecture, standards, and strategic direction of the data, analytics, and reporting platform.

Key Responsibilities

• Design, develop, and operate AI-ready data platform components that power next-generation intelligent experiences across Webex Contact Center.
• Partner with the Data Platform Architect to build the foundational intelligence layer that transforms enterprise contact center data into predictive insights, recommendations, and autonomous decision-making.
• Develop scalable data pipelines, feature engineering frameworks, and intelligent data services that enable training, deployment, and continuous improvement of custom AI and machine learning models.
• Build reusable AI platform capabilities including feature stores, inference pipelines, semantic search, vector retrieval, embeddings, knowledge augmentation, and model-serving infrastructure.
• Develop high-performance streaming and batch data processing systems capable of handling billions of customer interaction events with low latency and high reliability.
• Build APIs and platform services that expose AI-ready data to Reporting, Analytics, AI Agents, Routing, Workforce Optimisation, and other intelligent platform capabilities.
• Optimize AI and data platform performance across ingestion, feature computation, model inference, storage, retrieval, and serving to meet enterprise-scale latency and throughput requirements.
• Evaluate emerging AI technologies, machine learning frameworks, vector databases, and modern data infrastructure, rapidly prototyping new ideas that can differentiate Webex Contact Center.
• Participate in architecture discussions, design reviews, and technical innovation, contributing to the evolution of Cisco's AI-first data platform.
• Mentor engineers and champion engineering excellence through clean architecture, automation, observability, testing, and knowledge sharing.

Minimum Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related technical field.
• 8+ years of experience building distributed cloud-native SaaS applications.
• Strong software engineering expertise in Java and/or Python with experience developing production-grade backend services.
• Hands-on experience building modern data platforms using technologies such as Kafka, Flink, Spark, Iceberg, Pinot, ClickHouse, or equivalent streaming and analytical systems.
• Good understanding of modern AI and machine learning architectures, including feature engineering, embeddings, vector search, Retrieval-Augmented Generation (RAG), semantic retrieval, inference pipelines, and model-serving frameworks.
• Experience integrating machine learning models into large-scale production systems, with an understanding of model lifecycle, monitoring, evaluation, and continuous improvement.
• Strong understanding of distributed systems, event-driven architectures, microservices, and cloud-native application design.
• Experience building scalable data processing pipelines supporting both real-time and offline AI workloads.
• Experience optimizing large-scale systems for latency, throughput, scalability, reliability, and operational efficiency.
• Hands-on experience with AWS or GCP, Kubernetes, Docker, and cloud-native engineering practices.
• Strong understanding of data modelling, APIs, distributed storage, caching, and performance tuning.
• Excellent analytical, debugging, and problem-solving skills with the ability to work across complex enterprise platforms.

Desired Skills

• Experience building AI-powered enterprise platforms where machine learning is a core product capability rather than a standalone service.
• Experience with LLMs, fine-tuning, prompt engineering, agentic workflows, or domain-specific AI applications.
• Experience with vector databases, feature stores, knowledge graphs, semantic indexing, and intelligent retrieval systems.
• Familiarity with modern AI frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, MLflow, Ray, or equivalent technologies.
• Experience developing custom AI or machine learning models for recommendation systems, forecasting, anomaly detection, conversational intelligence, or decision optimisation.
• Experience optimizing GPU-accelerated inference, model-serving infrastructure, and large-scale AI workloads.
• Experience working on enterprise SaaS platforms handling large-scale customer data, preferably in Contact Center, Customer Experience, CRM, or communications platforms.
• Strong interest in applying AI to solve complex customer problems and creating differentiated product capabilities beyond commercially available AI models.