Large Language Models (LLMs), Deep Learning, Jenkins, Git, Docker, Cloud Services, Natural Language Processing, Python, Kubernetes, AgenticAI frameworks, LangChain, CrewAI, CI/CD tools, reinforcement learning, Strands SDK, MCP protocol, Data processing workflows, LangGraph, R, MLOps tools, Data pipelines, Google ADK

snowflake , Spark SQL, Cloud Architecture, Docker, Databricks, Azure, Python, Kubernetes, AWS, Security, Model serving, RAG, CI/CD, Observability

Machine Learning, Hadoop, Power Bi, Waterfall, Scala, Tableau, Asana, Jira, Deep Learning, Tensorflow, Data Science, Hive, Pytorch, Spark, Agile, Scrum, Python, scikit-learn, Ai, R, MS Project

CSS, Sql, HTML, Javascript, Python, Google Search Console, SEMrush, Screaming Frog, Sitebulb, Ahrefs, Technical SEO, GA4, Looker Studio

Web Content Accessibility Guidelines (WCAG) 2.2 AA, Apis, Performance, automation, platform engineering practices, enterprise product organisations, marketplace, ML-powered systems, Reliability, data platforms, modern software platforms, Ai, availability, cloud-native technologies, digital accessibility, platform commerce, large-scale distributed systems, Security, assistive technologies, Operational Excellence, SaaS-like platform

Devops, Linux, Distributed Systems, Cloud Infrastructure, Python, Network Operating Systems, Go, sonic

Software development lifecycles, DevOps practices, Digital Experience tools, Developer platforms

Distributed Storage Systems, Distributed Database Systems, Performance scalability and enterprise system architecture, Analytics, Training and deploying AI models, Agentic engineering using local and/or frontier models, Data Processing, CI/CD infrastructure for large-scale deployments, and AI/ML systems

Gcp, Terraform, Node.js, Azure, Python, Kubernetes, AWS, Go