data engineering , Scala, Sdlc, Devops, Test automation, Spark, Databricks, Azure, Python, AWS, Agile delivery, Infrastructure-as-code, AI-assisted engineering practices

Typescript, Automation Frameworks, Node.js, automated testing principles, test frameworks for web API, horizontal quality platforms

snowflake , Java, Cassandra, Cloudformation, Spring Boot, Kafka, New Relic, Jmeter, Docker, Terraform, MySQL, Ansible, Reactjs, Kubernetes, Aws S3, OverOps

.NET, Redux, Jira, React, Azure Functions, Docker, App Services, Agile, Microsoft Azure, Scrum, Kubernetes, Azure DevOps, ASP.NET Core

Java, Kafka, Sql, Microservices, Rabbitmq, Spring Framework, Docker, Spring Boot, MongoDB, Azure, Elastic Search, Jpa, Kubernetes, AWS, Etl, NoSQL databases, GCE, Serverless functions

.NET, Redux, Jira, React, Azure Functions, Docker, App Services, Agile, Microsoft Azure, Scrum, Kubernetes, Azure DevOps, ASP.NET Core

Ml, Kvm, C, Mongo, Postgresql, Containers, OpenStack, Ubuntu, Debian, Linux, Bash, Ceph, Kubernetes, Python, Go, Ai, LXD, qemu, LXC, deb, snap packaging

Pyspark, Jenkins, Databricks, Python, AWS, Azure DevOps, GitHub Actions, Pipelines

Software Engineering, Python, Apis, Performance Tuning, correlation IDs, failure recovery, Clients, versioning, Agentic AI patterns, latency, planning execution loops, regression suites, labels, metrics selection, integration engineering, evaluation discipline, observability, auth concepts, CI-friendly code, tool function calling, MCP servers, offline eval, debugging production issues, Packaging, tests, schema-driven tool integration, clean architecture, error instrumentation, structured logging, golden sets, experiment reporting, prompt system instruction design