Apis, Automated Testing, Microservices, Devops, Gcp, Distributed Systems, Azure, AWS, cloud-native architecture, CI CD, AI GenAI technologies, Java Spring Boot

graph databases , Continuous Integration, Pyspark, Test Driven Development, Angular, React, Neo4j, MySQL, Postgres, Python, AWS, Agile Methodology, Scrum, Sql, Spark, Restful Services, Databricks, JanusGraph, vector databases, Pinecone, Neptune, specification by example, LLMs, Anthropic, Teradata, Scaled Agile SAFE, Milvus, OpenAI

containerization , Python, Apis, MLops, Databricks, Kubernetes, embeddings, AI orchestration frameworks, vector databases, Azure OpenAI, CI CD, AWS AI ML services, semantic search, Large Language Models, model monitoring, RAG agentic AI systems, LLMOps, Anthropic, governance frameworks, prompt engineering, microservices architectures, Azure AI Services, knowledge retrieval, OpenAI, Gemini

snowflake , Java, Cloudformation, Scala, Data Warehousing, ELT, Git, Docker, Terraform, Spark, Data Governance, Databricks, Kubernetes, Python, AWS, Etl, Powerbi, Microsoft Azure, LLM-based systems, GenAI, RAG vector search, data observability

Ms Azure, Gcp, AWS, Microsoft.Net, open-source stack

Prometheus, Grafana, Datadog, New Relic, Terraform, Distributed Systems, Incident Management, Cloud Infrastructure, Splunk, Kubernetes, AWS, SRE, CI CD, Monitoring, observability

.NET, SQL Server, Deep Learning, Angular, FastAPI, Python, Flask, LangChain, LLMs, NLP techniques, OpenSearch, RAG systems, Pinecone, Microservice architectures, Llama Index, FAISS, SaaS application development, ML algorithms

.NET, domain driven design , Java, DevSecOps, Wso2, Integrations, AWS, Azure, Ai, GenAI, Microservices architecture, cloud-native architectures, Mules

Performance, Scalability, Unix, Design Patterns, System Design, Distributed Systems, Linux, Database structure, Availability, Backend technologies, Enterprise architecture best practices