Dr M.

The University of Dubai is a student-centered institution offering accredited undergraduate and graduate programs with industry-focused training and executive education.

• Align AI, information systems, and cybersecurity programs with enterprise practice (cloud, data, Gen AI, security operations).
• Guest lectures and industry modules on applied AI, solution architecture, and responsible generative/agentic systems.
• Mentor students on portfolios, internships, and careers across consulting, cloud, data, and security.

Rezilens is a company.

• Set technology direction, platform scale, and security operating model for client and product engineering.
• Build and lead engineering: hiring bar, delivery standards, secure-by-design SDLC, and roadmap with CEO and leadership.
• Own architecture for data, automation, GRC/cybersecurity, and agentic-AI products (AWS-primary multi-cloud).
• Partner with universities on curricula, guest lectures, and talent pipelines for agentic AI and security.

Macquarie University is a major Australian public research university focused on research, teaching, and industry partnerships.

• Support research and teaching at the intersection of data, AI, and industry practice.

MentorCruise connects aspiring mentees with industry experts for mentorship in Software Engineering, Product Management, AI, Design and Business.

• Provide one-on-one mentorship to mentees in software engineering, product management, and AI via MentorCruise.
• Offer career guidance, code and architecture reviews, and portfolio feedback to accelerate mentee careers.

Amazon is a global technology company providing e-commerce, cloud computing (AWS), and digital services.

• Led ANZ technical and commercial motion for accelerated compute (GPU/AI/ML), including Bedrock and SageMaker adoption.
• Ran nationwide enablement for field, partners, and customers on AI infrastructure and production inference paths.
• Advised enterprises on workload fit, inference economics, and landing zones tied to production scale.

• Partnered with strategic customers to design and implement high-performance, cost-efficient AI/ML platforms.
• Championed cloud architecture best practices for compute-intensive AI workloads, enabling scalable inference and training deployments.
• Enabled adoption of services like Amazon SageMaker, Bedrock, and DataZone for developer-centric use cases.
• Authored AWS Well-Architected Generative AI Whitepaper, guiding developers on efficient and scalable GenAI deployment.

• Partnered with AWS enterprise customers to deliver scalable, sustainable cloud-based AI solutions aligned with ESG goals.
• Evangelized sustainable AI and data architectures to developers and technical leaders through public speaking, whitepapers, and customer workshops.
• Authored 20+ thought leadership materials exploring the intersection of AI, data analytics, and sustainability.
• Collaborated cross-functionally with AWS solution architects and product teams to guide adoption of optimized compute architectures for AI/ML.
• Enabled sustainability-aligned AI transformations for customers like Deloitte Australia and Syngenta, fostering long-term technical relationships.

• Acted as technical advisor and mentor to developer teams across APAC, enabling adoption of modern data and AI architectures using AWS tools.
• Architected real-time analytics infrastructure for hospitality and construction tech customers, resulting in significant processing efficiency gains.
• Promoted usage of SageMaker and Glue through direct technical enablement, customer hackathons, and developer events.
• Supported over $2M in customer cost savings by aligning architectures with both business and technical AI goals.

• Led AI and analytics transformation programs for clients in energy, banking, and utilities, focusing on scalable, cloud-native AI deployments.
• Built advanced ML and data solutions across AWS, Azure, and GCP, guiding technical teams in adopting best practices for AI at scale.
• Created a fast data community of practice, training 100+ developers on modern streaming and embedded analytics platforms using AWS Kinesis and Azure Data Factory.
• Delivered data maturity assessments and strategic roadmaps, empowering organizations to become AI-ready across verticals.

• Managed cross-functional teams delivering ML and data platform solutions for telco, energy, and banking clients.
• Developed embedded AI use cases such as proactive technical support systems, reducing customer support cases by 35%.
• Simplified complex AI/ML architectures into engaging narratives for business and developer stakeholders.
• Designed and deployed real-time data pipeline monitoring and analytics for top-tier financial institutions.

• Developed cloud-enabled solutions for customers in analytics and machine learning domains.
• Developed and implemented data and analytics solutions for clients, including data pipelines, ETL, data warehousing, and data governance.
• Provided expert consultation to clients on data strategy, aligning data initiatives with business objectives.
• Conducted performance tuning and optimization of database queries.
• Developed data hub for a major logistics company enabling real-time tracking and analytics of 2000+ fleets.
• Created an enterprise information management platform for utilities provider streamlining data access across 50+ systems.

• Designed and implemented deep learning models—including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)—to interpret sensor data from smart home environments.
• Led development of AI solutions enabling elderly and disabled residents to live independently, leveraging real-time IoT data streams.
• Built big data architecture handling 10TB+ of sensor data across 100+ homes, enabling robust data pipelines for model training.
• Collaborated with interdisciplinary teams to deploy models in production-like environments, bridging the gap between research and application.

• Led the design and development of the circuitry for a cutting-edge medical device, ensuring compliance with industry standards and regulations.
• Built real-time visualization of data collected from the medical device, enhancing user engagement and experience.
• Developed Bluetooth-based stethoscope enabling remote patient monitoring and real-time data transmission.

• Conducted applied research on classical machine learning and neural network models to optimize integration of renewable energy into smart grids.
• Developed predictive models using techniques such as decision trees, SVMs, and multi-layer perceptrons (MLPs) to forecast energy generation and load balancing.
• Utilized IoT technology for real-time data acquisition, enabling high-fidelity training data for ML models.
• Published 10+ peer-reviewed papers on smart grid AI, data-driven energy modeling, and system optimization.
• Created hybrid data models combining rule-based systems with neural networks to improve accuracy of renewable energy forecasting by 20%.

• Researched and developed machine learning models—both classical and neural networks—for anomaly detection in power transformers.
• Applied supervised learning algorithms and feedforward neural networks on time-series data collected via industrial IoT sensors.
• Published 5+ peer-reviewed papers on ML applications in electrical engineering and predictive maintenance.
• Analyzed large datasets to detect patterns indicating equipment failure, laying the groundwork for intelligent monitoring systems.