AI ML technologies and frameworks, software testing methodologies and best practices, automated testing tools and frameworks, cloud-based AI ML platforms, data science principles and practices, Agile and DevOps methodologies, performance testing of AI ML applications

Orchestration, Machine Learning, Power Bi, PowerShell, Storage, Sql, Distributed Systems, Windbg, Python, remediation workflows, KQL, ETW, anomaly detection, AI-assisted analysis, Kusto, Windows Performance Analyzer, Azure services, experimentation, Statistics, monitoring systems, Deployment

.NET, Automated Testing, Microservices, React, Performance, Distributed Systems, Containers, Kubernetes, secure development, Clickhouse, AI-assisted engineering, observability

Automated Testing, Microservices, Distributed Systems, release management, Sdlc, Devops, Rest Apis, Azure, Agile delivery, cloud platforms, Production Support, CI CD

Microservices, Reactjs, Distributed Systems, Knowledge systems, Performance and scalability optimization, Prompt engineering, DevOps practices, Retrieval-based systems, Agent assist automation, RAG, CI/CD, Java Spring Boot, LLM-based applications

distributed caching , Java, RDBMS, Python, NoSQL DBs

Java, Apis, Spring Boot, Microservices, Rest Api, Terraform, Kubernetes, DevOps practices

Automation PowerShell Python Shell scripting REST APIs Terraform Ansible CI CD pipelines, Governance audit evidence access reviews control validation policy compliance risk remediation, HashiCorp Vault KV database secrets AppRole LDAP OIDC Kubernetes auth PKI transit dynamic secrets replication audit logging, Integrations ServiceNow SailPoint Active Directory LDAP SIEM monitoring platforms application onboarding workflows, Cloud Azure AWS hybrid cloud identity and access patterns