As a Senior AI and Machine Learning Solutions Architect with over eight years of experience, I have successfully transformed complex data into intelligent solutions. My career spans startups, scaleups, and enterprise companies, where I have primarily served as a technical consultant. My roles have included AI Engineer, AI Architect, and AI R&D Consultant. I have gained valuable experience across diverse industries, including finance, engineering, manufacturing, innovation, and telecom.
Senior AI/ML Consultant with 8+ years of experience transforming complex data into intelligent solutions. I specialize in agentic AI systems, enterprise RAG solutions, and scalable ML deployments for Fortune 500 companies and innovative startups.
Rennes School of Business, France
2023-2024Massachusetts Institute of Technology
2019-2023MIT Institute for Data, Systems and Society
2024UC San Diego
2021-2023University of Wisconsin
2021-2022Institute of Space Technology, Pakistan
2013-2017Multi-agent AI system for Samsung's wearable technology design optimization, combining generative AI with 3D geometric processing to achieve 92% accuracy in size prediction models.
AI-powered KYC verification system for regulated fintech startup with GDPR compliance framework and comprehensive audit trail capabilities.
Credit risk assessment platform for NCB Jamaica with ML-based scoring models and explainability features meeting Caribbean banking regulations.
End-to-end observability demo with synthetic microservices, Prometheus metrics, anomaly detection service, and Grafana dashboards. Features real-time monitoring and early-stage AIOps capabilities with live GitHub Pages demo.
Modular AIOps toolkit for log analysis with template extraction, semantic anomaly detection, error burst detection, and pattern drift detection. Features TF-IDF vectorization and Isolation Forest for intelligent log monitoring.
People who understand logic and philosophy get more value from LLMs. This isn't about being smart. It's about having tools to structure thinking that LLMs can amplify.
Most people use LLMs wrong. They treat them like search engines or autocomplete. The value isn't the final response. The value is the thinking process you do together.
Build evaluation before you build features. This isn't optional. LLM projects without evaluation infrastructure fail in production because nobody knows if the system actually works.
I'm always interested in discussing new opportunities, innovative AI projects, or potential collaborations in enterprise AI and data science consulting.