WNS, part of Capgemini, is an Agentic AI-powered leader in intelligent operations and transformation, serving more than 700 clients across 10 industries, including Banking and Financial Services, Healthcare, Insurance, Shipping and Logistics, and Travel and Hospitality. We bring together deep domain excellence – WNS’ core differentiator – with AI-powered platforms and analytics to help businesses innovate, scale, adapt and build resilience in a world defined by disruption.Our purpose is clear: to enable lasting business value by designing intelligent, human-led solutions that deliver sustainable outcomes and a differentiated impact. With three global headquarters across four continents, operations in 13 countries, 65 delivery centers and more than 66,000 employees, WNS combines scale, expertise and execution to create meaningful, measurable impact.
Agentic AI IntegrationPrimary focus will be to build, deploy, and optimize AI agents using frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and AutoGen. Implement orchestration strategies, multi-agent collaboration, tool integration, and memory/state management. Drive experimentation to create autonomous or semi-autonomous agents that solve real-world business workflows and support decision-making processes.MLOps & Performance OptimizationEstablish MLOps pipelines covering the complete model lifecycle, including training, CI/CD, monitoring, deployment, and retraining. Utilize tools such as Git, Docker, Kubernetes, and vector databases to ensure efficient and reliable deployments. Optimize resource utilization and infrastructure costs.Cross-Functional CollaborationPartner with engineering, data science, and product teams to align technical solutions with business objectives. Effectively communicate complex concepts to both technical and non-technical stakeholders. Stay current with industry advancements and drive innovation in GenAI and Agentic AI strategies.Skills & QualificationsRequiredStrong proficiency in Python, SQL, and GenAI frameworks such as LangChain. Hands-on experience building and deploying AI agents with orchestration, tool integration, and state management capabilities. In-depth knowledge of LLM architectures, RAG, embeddings, prompt engineering, vector databases, and Agentic AI patterns, including ReAct, tool-calling agents, multi-step reasoning, and guardrails. Experience with cloud platforms such as AWS, Azure, and GCP, as well as containerization technologies. Strong analytical, problem-solving, and communication skills. Experience with data integration using REST APIs, Google APIs, and SQL databases, with the ability to move data seamlessly across systems. Experience in web development using FastAPI, TypeScript, asynchronous programming patterns, React, Node.js, component-based architecture, hooks, state management, and streaming APIs (SSE/WebSocket).Preferred4+ years of hands-on experience working with LLMs and GenAI solutions in production environments. Exposure to Agentic AI frameworks and multi-agent workflows such as CrewAI, LangGraph, and AutoGen. Familiarity with MLOps practices and AI deployment best practices. Experience working on client-facing engagements or cross-functional AI initiatives. Publications, open-source contributions, or demonstrable projects showcasing expertise in AI agent development.
Graduate or above
