Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure to support our initiatives to develop data-driven solutions to difficult business challenges across BUs and HUs• As a member of Digital Transformation within WNS Data Driven Transformation COE team, you will be responsible to build AI Algorithms focused on achieving best in class business outcomes leveraging Cognitive Intelligence, AI ML, Generative AI, Analytics Engine & integrating it with other digital levers components and accelerators• You will be responsible to build solutions for business units through innovative new cognitive services solutions for the client’s business operations focused on driving cost efficiency, revenue generation and other client business outcomes• You will be responsible to work on large deals involving multiple digital levers i.e., AI/ML, advanced analytics, workflows & rules engine, immersive customer experience, complex integrations of client’s existing platforms/systems/products with our newly built solutions and accelerators. You will be involved in understanding the detailed process maps, process entitlement studies, finding the right white spaces where Cognitive Services/AI ML/ Advance Analytics can be applied, creating a strong transformation roadmap, and executing it on the ground to achieve the required outcomes and impact.• Work collaboratively with multiple internal & external stakeholders to build, publish & deploy AI solutions for the transformation roadmap by building the solution design, functional architecture, technical architecture, Components/Accelerators level architecture showing the integration mechanisms, deployment architecture.• Design and develop robust, scalable data pipelines for ingesting, transforming, and storing large volumes of structured and unstructured data.• Implement data processing solutions using Apache Spark, Apache Flink, Hadoop, and cloud-based services like AWS Glue or Google Dataflow.• Collaborate with data scientists and analysts to design efficient data models and schemas for analytics and reporting.• Build and optimize data warehouses and data lakes for quick and reliable access to data for business intelligence and analytics.• Ensure data quality and integrity through data validation, cleansing, and enrichment processes.• Deploy and maintain monitoring and alerting systems to ensure the reliability and performance of data pipelines and infrastructure.• Stay updated with emerging technologies and best practices in data engineering to enhance data processing capabilities and efficiency.