AI ML concepts, SAP AI Core

Github, PostgreSQL, Kafka, Jboss, Websphere, Docker, Application Server, Oracle, AWS, Databases, Cloud Services, Graphql, Agile Methodologies, REST, Gcp, MLops, Spark, Helm, Azure, data pipelines, machine learning frameworks, deployment tools, CI CD, GitHub Actions, API integration technologies, AI ML frameworks, cloud platforms, big data technology, AI-driven solutions

Scripting, Servicenow, Dell Boomi, Togaf, Agile, AWS, Csm, Itsm, ITOM, Soap, Integrations, REST, Gcp, Cmdb, Mulesoft, App Engine, Azure, Service Catalog, Safe, IntegrationHub, Csa, Acls, AI ML-driven features, SecOps, Flow Designer, MID, Virtual Agent, CIS, CTA, ITIL v4, App Engine Automation Engine, Predictive Intelligence, HRSD

Python, Gen AI, GenAI architecture, ML algorithm, Llm

containerization , AI ML, MLops, AWS, Data Architecture, Python, Azure, Gcp, Apis, cloud platforms, privacy governance, Security, AI ML frameworks, scalable deployment architectures, system integration patterns, responsible AI principles, solution governance

Sql, API design, Tensorflow, MLops, System Design, Pytorch, Data Architecture, Kubernetes, Python, Azure, Docker, OpenAI, Kubeflow, MLflow, AI ML solution architecture, Scikit-learn, AWS SageMaker, data engineering pipelines, Google Vertex AI, cloud-native architectures

Category Management Solutions, Python packages, Statistical forecasting, R, Performance tuning techniques, Blue Yonder Demand Fulfillment, Business Analysis, Assortment Planning, Allocation, Database structures, AI ML algorithms

Ml, Edi, Erp, Rpa, Tableau, middleware, Power Bi, Sap Ariba, Apis, GEP, Ivalua, cxml, Ai, Sirion, Jaggaer, Oracle Procurement Cloud, Source-to-Pay