About

Core Experience

  • Leadership, Mentorship, and Capability Building
  • Cloud Architecture (Azure, AWS, Hybrid)
  • Infrastructure as Code (Bicep, Terraform, ARM)
  • High Availability / Fault Tolerance / DR (ASR, Failover Groups)
  • Security & Compliance (CIS, Defender, Prisma)
  • Observability & Monitoring (Azure Monitor, AMA, DCR)
  • DevOps & CI/CD Governance
  • Leadership, Mentorship, and Capability Building
  • Operating Model Transformation (Reactive → Structured)
  • Vendor & Managed Services
  • Cloud Cost Management & Optimization

Tools

  • AI/LLM/GPU/Inference : TensorRT-LLM | Triton Inference Server | vLLM | Nsight Systems | CUDA | FlashAttention FMHA
  • AI Hardware/Compute : NVIDIA DGX Spark | NVIDIA GB10
  • Agent Orchestration : OpenClaw | NemoClaw | OpenShell
  • Developer Tools : Visual Studio Code | Cursor | Claude | Codex | Gemini | Continue
  • Local LLM / Model Serving : Ollama | llama.cpp | MLX
  • Container Runtime : Docker | Docker Compose | Docker Desktop
  • Cloud Infrastructure : Microsoft Azure | Amazon Web Services | Azure DevOps | Terraform | Bicep | Cloud Formation
  • Security & complaince : Prisma | Qualyis
  • Data & Sources : Google BigQuery | GDELT Project | FinBERT
  • Finance / Market Data API : Interactive Brokers | TrendSpider | Tastytrade | Yahoo Finance
  • Languages : Java, python, typescript, Nodejs

Currently Working On

  • NVDIA DGX / H100 / A100 Deployments and Optimization
  • Training & Inference Model Deployments in Azure and AWS
  • Agentic Development Langchain
  • Financial Models - Random Forest, LSTM, Neural Networks
  • MCP - Model Context Protocols
  • Responsible AI practices and bias evaluation metrics (SHAP, LIME)

Core Topics

  • Multi-agent orchestration frameworks (LangChain, Strands, Crew AI)
  • Model Context Protocol (MCP) and Agent2Agent integration
  • Experience with multi-model approaches and model selection criteria
  • Proficiency in NLP techniques including transformers, fine-tuning LLMs, and vector databases
  • Hands-on experience with data science tools like pandas, scikit-learn, keras, nltk, TensorFlow/PyTorch
  • Familiarity with Apache Spark and large-scale distributed datasets