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.

Key Responsibilities• Design and develop Agentic AI systems (multi-step reasoning, tool use, orchestration) that integrate natively into client applications and workflows.• Build, fine-tune, and evaluate ML/LLM models using established ML training platforms and pipelines.• Collaborate directly with client stakeholders and engineering teams to gather requirements, scope solutions, and translate business problems into AI-driven technical designs.• Integrate agentic workflows with client-native applications via APIs, SDKs, and platform-specific tooling (web, mobile, or enterprise systems).• Implement RAG pipelines, vector databases, and prompt/context engineering strategies to improve agent accuracy and reliability.• Own model/agent evaluation — defining metrics, running experiments, and iterating based on performance and client feedback.• Ensure solutions are production-ready: focus on scalability, latency, observability, cost, and security of deployed AI systems.• Work cross-functionally with data engineering, product, and QA teams to ship reliable, well-tested AI features.• Document architecture, model behavior, and integration patterns for internal teams and client handover.• Stay current with the agentic AI and ML tooling landscape and recommend adoption of relevant frameworks and platforms.Required Skills & Experience• 3–5 years of overall engineering experience, with hands-on experience building AI/ML-powered applications.• Practical experience building Agentic AI systems using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or equivalent.• Experience integrating AI/ML capabilities into client-native applications (web, mobile, or enterprise software) via APIs and SDKs.• Working knowledge of ML training platforms (e.g., SageMaker, Vertex AI, Azure ML, or similar) for model training, fine-tuning, and deployment.• Strong programming skills in Python; familiarity with JavaScript/TypeScript or a mobile/native stack is a plus for client-application integration.• Solid understanding of LLM fundamentals: prompt engineering, embeddings, RAG, vector databases (e.g., Pinecone, FAISS, Weaviate).• Experience with cloud platforms (AWS, Azure, or GCP) and containerized deployment (Docker/Kubernetes).• Familiarity with MLOps practices — model versioning, CI/CD for ML, monitoring, and observability.• Strong client-facing communication skills; comfortable presenting technical solutions to non-technical stakeholders.• Prior experience in an IT services/consulting environment, working across multiple concurrent client engagements, is highly preferred.

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.