Hi—I'm Elliot. I help modern engineers build scalable systems, products, and AI-driven autonomy.

Over the years, I have founded and led two innovative technology ventures, navigating the dynamic and fast-paced environments of startups while delivering high-impact, scalable software solutions across multiple domains, including finance, education, healthcare, and enterprise-grade applications. In my dual capacity as a founder and software engineer , I specialize in architecting and developing resilient, cloud-native SaaS platforms that address complex real-world challenges and drive sustainable, long-term business growth.

My technical expertise is deeply rooted at the intersection of advanced software engineering, artificial intelligence, machine learning, and data science. I leverage principles of clean architecture and domain-driven design (DDD) to build modular, maintainable, and extensible systems. I am proficient in designing distributed systems using microservices and event-driven architectures that ensure high availability, fault tolerance, and horizontal scalability, enabling platforms to adapt seamlessly to evolving business requirements and high user concurrency.

I have extensive experience with containerization (Docker), orchestration (Kubernetes), and deploying cloud-native applications using leading cloud providers such as AWS and Azure. My proficiency extends to building RESTful and GraphQL APIs with strong authentication and authorization mechanisms, integrating data storage solutions ranging from relational databases like PostgreSQL and SQL Server to NoSQL databases such as MongoDB and Redis for caching and fast data retrieval.

In the AI/ML domain, I design and implement end-to-end machine learning pipelines using frameworks like TensorFlow, PyTorch, and Scikit-learn. This includes data preprocessing, feature engineering, model training, hyperparameter tuning, deployment of models as scalable microservices, and continuous monitoring in production environments. I have hands-on experience with advanced ML techniques, including deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models , applying them to predictive analytics, natural language processing (NLP), and computer vision tasks.

I bring a product-centric mindset combined with systems thinking to bridge the gap between engineering and product management. This collaborative approach ensures the delivery of high-performance solutions aligned with user needs and business objectives, even under tight deadlines and shifting priorities.

In addition to my technical and leadership roles, I am deeply committed to knowledge sharing and talent development. I mentor aspiring engineers and tech founders, conduct workshops and create technical content, and contribute to building a thriving community focused on innovation, continuous learning, and professional growth.

My work ethos is driven by a relentless commitment to delivering measurable value, maximizing impact through data-driven decision-making, and fostering sustainable growth through scalable, maintainable technology ecosystems.