.png&w=256&q=75)
5
1
Kuwait
1 year of experience
I'm a cybersecurity professional who enjoys solving complex security challenges, building AI-powered applications, and creating tools that make security simpler and more accessible. I'm passionate about continuous learning, innovation, and turning ideas into practical products.

SignalSniper AI is an AI-powered financial intelligence platform designed to help traders understand market signals with clarity and confidence. It analyzes Forex indicators such as RSI, MACD, Bollinger Bands, moving averages, and market patterns to explain possible market direction. Unlike traditional tools that only provide BUY, SELL, or HOLD signals, SignalSniper AI delivers explainable insights including signal reasoning, confidence level, market trend, risk assessment, entry guidance, stop-loss, take-profit, and key factors influencing the decision. The platform is built on a multi-agent AI architecture where specialized agents analyze different market perspectives, including signal validation, risk management, contrarian analysis, market context intelligence, historical pattern analysis, and final decision orchestration. These agents collaborate to generate balanced, reliable, and risk-aware trading intelligence. SignalSniper Scoreβ’ Engine calculates a proprietary trade intelligence score and generates a professional AI Trade Brief in HTML and Markdown from multi-agent analysis. SignalSniper AI combines technical analysis with generative AI to transform complex financial data into easy-to-understand insights, helping traders improve their analysis process and make more informed decisions. The Multi-Agent Decision Orchestrator combines individual agent outputs into precise trading intelligence while reducing single-model dependency through collaborative AI reasoning. SignalSniper AI demonstrates the use of artificial intelligence in FinTech by making market analysis more accessible, transparent, and user-friendly. It uses a hybrid AI architecture powered by Fireworks GPT, self-hosted Gemma running on AMD ROCm-accelerated PyTorch with vLLM. A Mock Gemma fallback ensures reliable analysis availability, enabling scalable AI deployment for algorithmic trading and intelligent decision support systems.
11 Jul 2026