Lead Analyst - Dgtl. Trnf.

Company Description

WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence.We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400+ clients with the help of our 44,000+ employees.

Job Description

KEY RESPONSIBILITIES Responsible for development of data-driven solutions across BUs and Hus.• Develop and maintain CI/CD pipelines for software applications and AI/ML model deployments.• Deploy and scale GenAI models using Kubernetes, Docker, and serverless computing.• Optimize cloud infrastructure (AWS, Azure, GCP) for AI/ML workloads.• Automate infrastructure provisioning with Terraform, Ansible, CloudFormation.• Monitor AI model performance and system health using Prometheus, Grafana, ELK Stack.• Implement MLOps best practices for continuous model training, versioning, and monitoring.• Ensure security compliance, API governance, and ethical AI standards.• Collaborate with Data Science & AI teams to streamline AI model deployment.• Manage GPU clusters, vector databases, and inference endpoints for GenAI applications.

Qualifications

• B.E./B.Tech. (Computer Science/IT/any related) from a premier institute would be preferred• Experience with LLM fine-tuning, AI inference optimization, LangChain, and RAG pipelines.• Certifications like AWS Certified DevOps Engineer, CKA (Certified Kubernetes Administrator), Google Cloud ML Engineer are a plus.• Experience with serverless AI architectures (Lambda, Cloud Run, Fargate).