Slm, Togaf, AI Architecture, data management frameworks, Agentic AI architectures, vibe coding tools, multi-agent orchestration, embedding models, vector databases, Claude, Responsible AI frameworks, prompt engineering, Llm, Gen AI, Google Vertex AI, Azure AI, cloud AI platforms, OpenAI, Amazon Bedrock, RAG architectures

.NET, Ml, Paas, Restful Api, Microservices, Angular, React, Azure, Azure DevOps, AI orchestration frameworks, GenAI, LLMs, event-driven systems, Ai, IaC, prompt engineering, GitHub Actions, enterprise integration patterns, RAG architectures

Cursor, CrewAI, LLMOps, Claude Code, vector databases, Semantic Kernel, AutoGen, LangGraph, GitHub Copilot, evaluation pipelines, AgentKit, AI observability, RAG architectures

data engineering , Ml, Distributed Systems, Microservices, Devops, Data Science, Apis, Prompt engineering, AI Automation, Infrastructure automation, Ai, Vector databases, GenAI, LLM APIs, Agentic workflows, Cloud-native architectures, MLOps practices, Orchestration frameworks, RAG architectures, CI CD pipelines

lifecycle management , Data Governance, Devops, Testing, MLops, Coding Standards, enterprise-grade production systems, compliance frameworks, SRE, orchestration frameworks, LLMOps, Agentic AI solutions, AI-first architectures, software engineering excellence, Agentic workflows, prompt management, data-intensive systems, observability, real-time decisioning, Model integration evaluation, agent lifecycle management, LLM-powered applications

Gcp, Azure, AWS, LangChain, CrewAI, Prompt engineering, Cloud-native architectures, LangGraph, AutoGen, Generative AI ecosystems, Multi-agent systems, Tool integration

model selection , agent orchestration frameworks, data pipelines, RAG pipelines, AI evaluation techniques, LLM integration, architectural tradeoffs, Agentic AI workflows, performance metrics, AI-driven capabilities, predictive ML models

Tensorflow, Gcp, Pytorch, MLops, Azure, Python, AWS, LangChain, vector databases, RAG, ML CI CD, GenAI frameworks