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

Power Bi, PowerShell, Sql, Machine Learning, Python, Windbg, KQL, ETW, AI-assisted analysis, Kusto, Windows Performance Analyzer, Azure services

Kafka, Gcp, DevSecOps, Elasticsearch, Azure, AWS, Opensearch, CI CD, Druid, automation-based delivery, system monitoring solutions, AI ML technologies

.NET, C#, React, SQL Server, Azure cloud services, Angular, Azure DevOps, CI/CD pipelines and deployment practices, Code review processes and quality standards, Jira or similar project management tools

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

backstage , Github, Amazon Web Services, Apis, Prometheus, Grafana, Datadog, Web Hosting, Terraform, Ansible, Openshift, Dynatrace, Microsoft Azure, Containers, Splunk, automation, Kubernetes, Cribl, Observe

distributed caching , Java, RDBMS, Python, NoSQL DBs