Cloud Engineer - 8+yrs - Cloud-native SaaS applications, Java, Python, Kafka/Flink/Spark/Ice berg
Cisco · Bengaluru
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 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. You will design and develop scalable, secure, reliable, and future-ready data capabilities 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
- 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.
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