graph databases , data engineering , Python, Databricks, Sql, Devops, Agile Methodologies, Rest Apis, Pandas, Etl, Nosql, Gcp, ELT, Microservices, Automated Testing, Spark, LLMs, LangChain, RAG, semantic retrieval, LangGraph, GPT, cloud-native applications, AI LLMOps, Agentic AI, Generative AI

System Design, Testing, Python, Application Development, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems

Datadog, AWS, Redis, Spring Boot, Java, Kubernetes, Postgres, Helm, Kafka, LangChain, Temporal, LangGraph

Google Cloud Platform (GCP), Rpa Uipath, Databases, Apis, Enterprise Applications, Python, Agentic AI, CI/CD pipelines, Tool calling orchestration frameworks, LLM-based agents

MCP (Model Context Protocol), snowflake , Github, Machine Learning, Databricks, Azure, Software Engineering, Testing, AWS, Unity Catalog, AI-assisted development tools, AI application frameworks, Modern AI interfaces and standards, Model and tool integrations, CI/CD practices, Statistical and machine-learning concepts

Application Development, System Design, Testing, Python, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems

Visual Studio, Azure Sql, Azure cloud services, orchestration templates, vector search, security across application and data layers, observability in GenAI applications, scalable microservices, reusable components, DevOps practices, API design principles, cloud-native delivery, model safety, prompt libraries, prompt optimization, LLM-integrated systems, micro front-end architecture, multi-step task orchestration, context management, Cost Optimization, containerized workloads, SDKs, front-end applications familiarity

Ml, Cloud Migration, Software Engineering, multi-agent systems, Generative AI, application transformation, LLMs, Ai, AI-assisted application modernization, Security, Agentic AI, platform upgrades, responsible AI practices, Governance, observability, RAG architectures, autonomous agents