Job Description
Company Description
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.
Job Description
About the RoleWe are looking for a detail-oriented Senior Associate – Data Automation & AI Operations to support and maintain production automation and AI-driven data processing workflows. This role involves monitoring Python-based automation pipelines, troubleshooting production issues, and assisting in optimization of LLM-enabled and document/data processing systems.This role is ideal for professionals with 1+ years of experience in automation, data engineering, or AI operations who are comfortable working in production environments and collaborating with cross-functional teams.Key ResponsibilitiesAutomation & Production SupportMonitor and support production automation pipelines developed using Python and FastAPIPerform L1/L2 troubleshooting for automation scripts, APIs, and data workflowsIdentify and resolve script failures, dependency conflicts, API errors, and logic edge casesAnalyze logs, error traces, and performance issues in distributed systemsData Processing & ValidationProcess and validate structured and semi-structured data from multiple sources (Excel, CSV, APIs)Perform data normalization, deduplication, and quality checksInvestigate and resolve data inconsistencies impacting downstream workflowsAI / LLM OperationsSupport LLM-based automation workflows including prompt optimization and token managementAssist in RAG systems by validating embeddings and debugging semantic search relevanceReview and improve LLM output qualityDevOps, Tools & CollaborationUse Git for version control (branching, pull requests, conflict resolution)Perform basic Docker operations such as container inspection and log analysisTest and debug APIs using Postman and cURLPrepare incident reports, root cause analysis (RCA), and escalation summariesCollaborate with business, data, and engineering teamsSkillsetMandatory SkillsPython (Advanced) – automation, scripting, debuggingData Wrangling & Data AnalysisProduction automation FastAPI or similar backend frameworksLLM workflows, prompt engineering, and RAG conceptsVector databases exposure (e.g., Pinecone)Git, Docker, Postman, cURL cloud platform exposureExcelCommunication SkillsBehavioral SkillsStrong analytical and problem-solving mindsetAttention to detail Ownership and accountabilityOptional / Additional SkillsetOCR SQL, Power Bi, JiraSemantic search and AI model evaluation knowledge
Qualifications
Bachelor's degree in Engineering, Computer Science, Data Science, AI or related field1–4 years of experience in automation, data engineering, or AI operations roles
