I specialize in AI Platform Engineering: designing the orchestration, observability, and security layers needed to run LLMs at scale. I leverage production-grade Python to build deterministic pipelines using modern ETL frameworks (Dagster, Prefect, Airflow) and Docker/Kubernetes, ensuring models are not just intelligent, but resilient and secure by design.
Core Engineering Focus:
- ▹ Production MLOps & Orchestration
- ▹ Agentic AI Architecture
- ▹ AI Platform Engineering
- ▹ Secure-by-Design Systems
Check out my latest engineering writeups below or browse through all articles .
Featured Engineering & Research
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Building RedTeam MCP: An AI-Powered Penetration Testing Assistant
A practical guide to building an MCP server that enables AI assistants to orchestrate offensive security tools for penetration testing, with proper safety guardrails
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Building LangChain Tools and Agents: From Zero to SOAR Assistant
Learn how to create LangChain tools from scratch and build a simple SOAR (Security Orchestration, Automation and Response) agent. This hands-on tutorial covers the fundamentals of tool development and agent creation for security automation.
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Building a RAG System for Cybersecurity Compliance: A Simple POC with LangChain v1+
A step-by-step walkthrough of building a basic RAG system with LangChain v1+ to query compliance regulations. This is an unoptimized POC for learning purposes, not production-ready.
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LangGraph Agent Architectures and Patterns: A Professional Guide
A comprehensive theoretical guide on agent architectures and patterns used professionally in AI projects. From simple workflows to hierarchical multi-agent systems, with real-world use cases for each pattern.
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Model Context Protocol (MCP): Bridging the Gap Between AI and External Systems
An in-depth exploration of Model Context Protocol (MCP), the open standard revolutionizing how AI systems interact with external data sources and tools
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Adversarial Machine Learning: Attacks and Defenses
Deep dive into adversarial attacks against ML models: evasion, poisoning, and extraction. Exploring defenses, red teaming strategies, and the MITRE ATLAS framework for securing AI systems.
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Vector Embeddings and Semantic Search: The Foundation of Modern AI
A comprehensive exploration of vector embeddings, from word2vec to modern transformers, and how they enable semantic search in production systems
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Understanding LLM (Large Language Models): From Transformers to GPT
A deep dive into the architecture, mechanisms, and evolution of Large Language Models, from the Transformer breakthrough to modern GPT systems
Latest Posts
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Engineering Security ML with Elastic – Part 4: Production Pipelines with Dagster & MLflow
Transitioning from exploratory notebooks to reliable, repeatable anomaly detection pipelines using Dagster and lightweight MLflow-based model management.
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Engineering Security ML with Elastic – Part 3: Benchmarking Unsupervised Models
How to Compare Anomaly Detection Models Without Labels
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Engineering Security ML with Elastic – Part 2: Unsupervised Anomaly Detection
Learning Normal Authentication Behavior with Autoencoders
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Engineering Security ML with Elastic – Part 1: From Logs to Features
From Windows Event Logs to Behavioral Features: Preparing Elasticsearch Data for Anomaly Detection
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TypeScript for Pythonistas: A Guide to Building Red Team Tools
A comprehensive TypeScript guide for Python developers. Learn the fundamentals by comparing Python and TypeScript code, focused on offensive security tool development and MCP servers.
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Docker & Kubernetes Abuse Cheatsheet
Container escapes, docker.sock exploitation, K8s privilege escalation and misconfigurations for HTB, CTFs and cloud pentests