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

.NET, Ml, Git, Uipath, Python, Sql, Ai

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

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, PostgreSQL, Scala, Spring Boot, Kafka, Emr, JIRA, Jenkins, Git, Docker, Reactjs, Spark, Apache Camel, MongoDB, Couchbase, Kubernetes, AWS, DocumentDB, Automated deployment, EKS, Harness, CI/CD pipelines