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Skills & Expertise

Technical Skills & Professional Expertise

Comprehensive skill set spanning AI development, cloud computing, and community leadership

Artificial Intelligence & Machine Learning

Core AI Technologies

  • Azure AI Platform

    Expert Level - Azure AI Studio, Azure OpenAI Service, Azure Cognitive Services

    • 15,000+ views on Azure AI Studio educational content
    • Production implementations and architecture design
    • Complete ecosystem expertise from setup to deployment

  • AI Agents & Frameworks

    Advanced Level - AutoGen, Semantic Kernel, Azure AI Agent Service

    • 10-part educational series on AI agents development
    • Multi-agent system design and orchestration
    • Production-ready agent implementation patterns

  • Large Language Models

    Expert Level - Prompt Engineering, RAG, LLM Operations

    • Advanced prompt engineering techniques
    • Retrieval-Augmented Generation implementations
    • Quality assurance and operational excellence

  • Computer Vision & NLP

    Proficient Level - Azure Cognitive Services, Custom Models

    • Multi-modal AI service integration
    • Custom vision and language model development
    • Production computer vision applications

AI Development Skills

Python
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# Skill proficiency overview
ai_skills = {
    "prompt_engineering": "Expert",
    "rag_implementation": "Expert", 
    "agent_development": "Advanced",
    "llm_operations": "Advanced",
    "model_fine_tuning": "Intermediate",
    "computer_vision": "Proficient",
    "nlp_processing": "Proficient"
}

Cloud Computing & Azure Services

Azure Expertise

Service Category Skill Level Key Projects
Azure AI Studio Expert 15,000+ views educational guide
Azure OpenAI Expert Production LLM implementations
Azure Cognitive Services Advanced Multi-service integrations
Azure Functions Advanced Serverless AI applications
Azure Cosmos DB Proficient Vector database implementations
Azure DevOps Proficient CI/CD for AI applications

Cloud Architecture

  • Scalable AI Solutions - Multi-region deployment strategies
  • Cost Optimization - Efficient resource management and scaling
  • Security & Compliance - AI application security best practices
  • Performance Monitoring - Application insights and optimization

Programming Languages & Technologies

Primary Languages

Languages: Python, Jupyter Notebook, Java
AI/ML: Azure OpenAI, Semantic Kernel, AutoGen, scikit-learn, Hugging Face Transformers, TensorFlow/PyTorch, Kaggle pipelines, NumPy, Pandas, Matplotlib, Seaborn
Agentic Frameworks: MCP (Model Context Protocol), FastMCP, Semantic Kernel Agents, Agentic RAG, AutoGen, LangChain, AI Agents Service, Azure AI Foundry
Frameworks/Tools: FastAPI, Flask, Streamlit, Chainlit, Mediapipe, PyAutoGUI, OpenCV
Cloud & APIs: Azure, Azure Cosmos DB, Azure Static Web Apps, Vercel, GitHub Pages, REST APIs
DevOps: Docker, GitHub Actions, Vercel auto-deploy, GitHub Pages deployments, VS Code, FastAPI
Databases: MongoDB, ChromaDB, Azure Cosmos DB (experimental)

  • Python

    Expert Level - AI/ML development, FastAPI, data processing

    Python
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    # Production AI application example
from fastapi import FastAPI
from azure.ai.openai import AzureOpenAI

app = FastAPI()
client = AzureOpenAI(endpoint=endpoint, api_key=key)

  • TypeScript/JavaScript

    Advanced Level - Full-stack development, VS Code extensions

    TypeScript
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    // VS Code MCP extension implementation
interface MCPProtocol {
    handleRequest(request: MCPRequest): Promise<MCPResponse>;
}

  • C#/.NET

    Proficient Level - Enterprise applications, Azure Functions

    C#
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    // Azure Function for AI processing
[FunctionName("ProcessAIRequest")]
public static async Task<IActionResult> Run(
    [HttpTrigger] HttpRequest req, ILogger log)

Frameworks & Tools

  • Web Frameworks: FastAPI, React, .NET Core, Express.js
  • AI/ML Libraries: LangChain, Semantic Kernel, AutoGen, Chainlit
  • Development Tools: VS Code, Git, Docker, GitHub Actions
  • Databases: Azure Cosmos DB, PostgreSQL, Vector Databases

Technical Writing & Documentation

Content Creation Expertise

  • 68,605+ total views across Microsoft Tech Community articles
  • 16 published articles covering complex AI and Azure topics
  • Educational Content Design - Making complex concepts accessible
  • Technical Documentation - Comprehensive guides and tutorials

Writing Skills

Content Type Examples Impact
Tutorial Articles Azure AI Studio Guide 15,000+ views
Technical Deep-Dives Contoso Chat & LLM Ops 12,500+ views
Implementation Guides AI Agents Development 20,905+ series views
Best Practices Prompt Engineering 6,800+ views

Project Management & Leadership

Community Leadership

  • Microsoft MVP - Recognized community leadership
  • Microsoft Learn Student Ambassador - Technical community building
  • Workshop Facilitation - 25+ technical workshops conducted
  • Mentorship - 100+ developers guided and mentored

Project Management

  • Open Source Projects - Leading collaborative development
  • Content Strategy - Educational content planning and execution
  • Community Programs - Workshop and mentorship program management
  • Cross-functional Collaboration - Academic and industry partnerships

Emerging Technologies & Continuous Learning

Current Learning Focus

  • Model Context Protocol (MCP) - Advanced implementations and integrations
  • Multi-Agent Systems - Complex agent orchestration and coordination
  • AI Safety & Ethics - Trustworthy AI development practices
  • Performance Optimization - Scalable AI application architecture

Future Skill Development

  • Advanced MLOps - Production ML pipeline optimization
  • Edge AI - Mobile and IoT AI implementations
  • Quantum Computing - Preparation for quantum AI algorithms
  • AI Governance - Enterprise AI strategy and compliance

Skill Validation & Certifications

Microsoft Certifications

  • Azure AI Fundamentals (AI-900)
  • Azure Fundamentals (AZ-900)
  • Azure Developer Associate (AZ-204) In Progress

Community Recognition

  • Microsoft MVP - Annual recognition for community contributions
  • 68,605+ article views - Validated educational impact
  • Global adoption - Content used in university curricula
  • Production implementations - Real-world project success

Continuous Assessment

  • Peer Review - Regular feedback from fellow MVPs and community
  • Project Outcomes - Measurable impact of developed solutions
  • Community Feedback - Direct feedback from workshop participants and mentees