AI Solution Architect & Consultant

Led and advised on advanced AI/ML initiatives as a Senior Manager and independent consultant, driving strategic vision, architectural design, and hands-on development of cutting-edge solutions from PoC to production.

• Spearheaded Agentic AI initiatives, guiding multiple clients from conceptualization to successful Proof-of-Concept (PoC) and Minimum Viable Product (MVP) deployments, significantly accelerating AI adoption strategies.
• Architected and implemented complex AI solutions across diverse domains, including Computer Vision for automotive applications, NLP for document classification, and Reinforcement Learning for industrial process optimization.
• Provided comprehensive AI advisory services, conducting AI audits, developing best practices, and formulating data-driven strategies that resulted in a 25% improvement in client project efficiency and ROI.
• Secured and contributed to multiple patents in Automotive AI and Computer Vision, demonstrating innovative problem-solving and establishing intellectual property in highly competitive markets.
• Managed end-to-end AI project lifecycles, ensuring seamless integration of models, robust data pipelines, and scalable deployment strategies that enhanced operational capabilities for enterprise clients.

Developed and deployed advanced machine learning models for industrial applications, specializing in optimizing complex systems through data-driven insights and innovative algorithms.

• Designed and implemented a Reinforcement Learning system for optimizing industrial water processes, leading to a 15% reduction in energy consumption and a 10% improvement in water quality metrics.
• Developed and integrated traditional ML models with IoT sensor data, enabling predictive maintenance that reduced unscheduled downtime by 20% across manufacturing facilities.
• Collaborated with cross-functional engineering teams to translate operational challenges into scalable AI solutions, improving system efficiency and reducing operational costs by an average of $500K annually.
• Pioneered the application of machine learning techniques to enhance sensor data analysis, improving data accuracy by 30% and providing more reliable inputs for critical decision-making.

Focused on research and development of cutting-edge Computer Vision and Natural Language Processing solutions for next-generation automotive systems.

• Developed advanced Computer Vision algorithms for autonomous driving features, contributing to a 98% accuracy rate in object detection and classification in diverse environmental conditions.
• Authored and secured multiple patents in the Automotive AI domain, specifically for novel approaches to real-time image processing and sensor fusion, enhancing vehicle safety systems.
• Implemented Document Classification systems using NLP techniques, including FastText, to process and categorize vast amounts of unstructured text data, improving information retrieval efficiency by 40%.
• Contributed to the development of a proprietary ML model training pipeline, reducing model iteration cycles by 25% and accelerating the deployment of new AI features into prototypes.

Developed a modular Agentic AI framework for a B2B SaaS client, designed to automate complex customer support queries and escalate nuanced issues to human agents.

Implemented a Reinforcement Learning solution to dynamically optimize chemical dosing and energy consumption in industrial water treatment plants.

Led the development of a real-time pedestrian detection system for autonomous vehicles, enhancing safety and situational awareness.

Developed an automated document classification system for a legal firm to streamline the categorization and retrieval of legal documents.

Awarded multiple patents for innovations in Computer Vision and real-time image processing applications within the automotive sector, contributing to advancements in vehicle safety and autonomous systems.

A deep dive into the practical applications and strategic implications of Agentic AI for businesses, covering architectural patterns and deployment considerations.

A technical paper detailing the methodology and results of applying Reinforcement Learning to achieve significant efficiency gains in industrial water treatment facilities.