Jenkins, Gcp, Azure, Python, AWS, Azure DevOps, LangChain, LLMs, cloud platforms, vector databases, Pinecone, REST API design, LangGraph, GitLab CI, GitHub Actions, RAG techniques, microservices architecture, CI CD tools, Generative AI libraries, Weaviate, dataset preparation

Azure Cloud, Generative AI, LLMs, AI Agents, containerized deployments, vector databases, MCP Agent-to-Agent communication, AI Search APIs, prompt engineering, Security, RAG, hallucination mitigation, Azure AI, AI architecture evaluation, AI observability, Governance, AI orchestration

knowledge graphs (neo4j preferred), Python, AWS Bedrock, prompt-library version-management, prompt engineering, vector stores, LLM GenAI applications, multi-agent systems, Caching, prompt compression, LLM guardrails, model routing

Neo4j, Sql, AWS, Python, Azure, Gcp, LLMs, Cypher, LangChain, Knowledge Graphs, LangGraph, Amazon Neptune

Generative AI (GenAI), Docker, Rest Apis, Python, LangChain, Agentic AI, RAG architecture, microservices architecture, containerized deployments, vector databases, LlamaIndex

Restful Apis, Databricks, Microservices, Automated Testing, Django, React, Angular, FastAPI, Cybersecurity, Python, Data Privacy, Flask, Microsoft Azure, AI governance, Claude, Regulatory Compliance, fine-tuning, vector databases, Next.js, observability, AI model evaluation, OpenAI APIs, Anthropic APIs, ChatGPT

Power Bi, Pyspark, SQL Server, Apache Spark, Kafka, Tableau, Redis, Python, objectstore, RDBMS technologies, Iceberg, Data Layer Lakehouse, Delta SQL

LangChain

Machine Learning, Data Analytics, Deep Learning, Python, LLM-based workflows