About me
I am a Principal AI Architect and Software Engineer specializing in high-performance System Design and Distributed AI Infrastructure. With a career built on the foundation of translating complex research into production-ready technology, I bridge the gap between academic innovation and global scalability.
As the Founder of Omnisoftex and Ranea.ai, I have spent years architecting AI-first platforms that solve real-world "impossible" problems—specifically in the healthcare and communication sectors. My engineering philosophy is centered on resilience, low latency, and health equity, ensuring that the most advanced AI tools are accessible to the communities that need them most.
Core Expertise & Technical Focus
AI Infrastructure & Orchestration
Designing end-to-end ML pipelines and high-concurrency backends for real-time inference.
System Design & Architecture
Expert-level knowledge in building distributed systems, microservices, and cloud-native environments capable of handling millions of interactions.
Research Leadership
As an American Cancer Society (ACS) Scholar, I lead a $50,000 research initiative at Brooklyn College (CUNY) focused on utilizing Machine Learning to advance cancer survivorship and mental health outcomes.
NLP & Voice AI
Developing multilingual, human-like AI communication layers that reduce operational load and eliminate language barriers in global markets.
Innovation Driven by Mission
My work is more than just code, it is a response to a global need for better infrastructure.
Proven Impact
Under my leadership at Omnisoftex and Ranea.ai, we have processed over 13,000+ clinical interactions, significantly reducing hospital no-show rates and staff burnout.
Academic Rigor
My research at the Brooklyn College Cancer Center (BCCC-CURE) pushes the boundaries of how AI can serve marginalized populations, ensuring no community is left behind by the digital revolution.
Technical Toolkit
Languages: Python, C++, Go, TypeScript.
AI/ML: LLM Fine-tuning, Natural Language Processing (NLP), Predictive Analytics, PyTorch.
Infrastructure: AWS, Google Cloud, Kubernetes, Docker, Distributed Databases, API Design.
Specialties: System Design, Scalable Software Engineering, MedTech Innovation, AI Research.
Experience
AI/ML Engineer |Research Scholar
Designed and deployed an NLP pipeline using LangChain and Hugging Face, improving chatbot accuracy by 32% and reducing internal support resolution time by 40%, enabling proactive cognitive support for cancer survivors.
Integrated OpenAI-based Retrieval-Augmented Generation (RAG) architecture to support legal and educational document understanding, improving retrieval relevance by 35% and reducing research time for compliance and care teams by 20%.
Led reinforcement learning experiments using PyTorch and LIDAR simulation data to model task performance related to cognitive well-being, enhancing object detection efficiency and reducing task completion time by 28% in simulated recovery environments.
Founder/AI/ML Engineer
Trained CNN and LSTM models for OCR document classification, raising accuracy from 81% to 93% in loan application processing workflows.
Created APIs with FastAPI for ML model endpoints, improving runtime access for production applications and reducing failure rates during deployments by 33%.
Engineered MongoDB schema for text analytics, supporting faster query execution and reducing storage redundancy for document- based datasets by 18%.