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Built a dependency-free Python CLI that prioritizes synthetic incidents, classifies severity, and recommends evidence-based diagnostic next steps.
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This project focused on building and testing a practical AI security assessment lab for evaluating LLM defenses against prompt injection and jailbreak attacks.
I integrated Spikee by Reversec with a locally hosted cybersecurity model running through LM Studio, then added NVIDIA NeMo Guardrails to compare model behavior under three conditions: no guardrails, input filtering, and combined input/output protection.
The work included configuring the local model environment, building a custom FastAPI gateway, integrating NeMo Guardrails, troubleshooting model latency and timeout issues, creating a reusable Spikee target, and analyzing attack results using Spikee’s built-in reporting tools.
The project also explored different adversarial testing approaches, including prompt injection datasets, obfuscation, encoded attacks, Best-of-N testing, synthetic canary leakage tests, and structured benchmark comparisons.
The objective was to measure how much the guardrails reduced successful attacks while keeping the model, dataset, and testing conditions consistent.
Tools used: Spikee, NVIDIA NeMo Guardrails, LM Studio, Python, FastAPI, PowerShell, Parrot OS, local LLMs, JSONL datasets, and custom security testing scripts.
This project demonstrates a hands-on approach to LLM red teaming, AI safety testing, prompt-injection assessment, and guardrail validation for organizations deploying generative AI systems.
pretty cool tbh. testing the same attacks with different guardrail setups makes the results way more interesting.
I Build Production-Ready AI Applications, Agents & Automation for Businesses
I’m currently open to new AI development projects and B2B collaborations.
I help companies turn business problems into practical, production-ready AI solutions — from the initial idea and architecture to development, cloud deployment, and integration with existing systems.
My work includes:
AI Agents & LLM Applications — GPT, Claude, Gemini, multi-agent workflows
RAG & Knowledge Systems — chat with company documents, internal knowledge bases, semantic/hybrid search
Business Automation — AI-powered workflows, document processing, email automation, CRM/ERP integrations
Custom AI Platforms — complete web applications with AI capabilities
Data Engineering — ETL/data pipelines, SQL, BigQuery, PostgreSQL and data preparation
Cloud Architecture — AWS, GCP and Azure
Backend & Full Stack — Python, FastAPI, React, TypeScript, APIs and integrations
I’ve worked on solutions involving logistics and supply chain, enterprise knowledge systems, document intelligence, operations, healthcare-related platforms, and AI-assisted software analysis.
I can help whether you already have a detailed specification or simply have a business problem and want to determine where AI actually makes sense and how to build it.
Available for freelance projects, B2B collaborations, MVP development and longer-term partnerships.
If you’re building something with AI or have a process you think could be automated, send me a message.
Have an AI project in mind? Let’s discuss what we can build.
Check my website: https://ai-consulting-site-hstz.vercel.app/
Impressive how you've turned complex logistics challenges into production-ready AI agents, and your AI-powered document processing will deliver strong results for any B2B partner. Looking forward to seeing your next integration with ERP systems.
𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐓𝐫𝐚𝐝𝐢𝐧𝐠 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 | 𝐏𝐲𝐭𝐡𝐨𝐧, 𝐅𝐚𝐬𝐭𝐀𝐏𝐈 & 𝐋𝐋𝐌
𝐌𝐲 𝐫𝐨𝐥𝐞: Python Backend Engineer focused on AWS serverless architecture
𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐝𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧:
I built a serverless backend for an AI-driven trading platform handling real-time market data and analytics. I used AWS services like Lambda, API Gateway, RDS, and S3 to create a system that scales without manual infrastructure management. I designed pipelines for ingesting and processing live data to support trading signals and sentiment analysis. My focus was on keeping latency low, handling high concurrency, and ensuring the platform remained stable for global users.
