Earned certification from NVIDIA demonstrating advanced proficiency in prompt engineering for large language models (LLMs). Acquired deep knowledge of prompt design principles, including contextual framing, role assignment, token efficiency, and iterative refinement. Applied these techniques to practical use cases such as summarisation, code generation, question answering, and conversational AI. Gained hands-on experience in guiding LLMs to produce accurate, reliable, and domain-specific outputs. This certification reflects strong competence in leveraging generative AI and natural language processing for building scalable, intelligent applications.

Earned certification demonstrating foundational understanding of AWS Cloud concepts, global infrastructure, and core services including compute, storage, networking, and databases. Gained knowledge of security best practices, pricing models, billing, and AWS support plans. Validated ability to explain the AWS Cloud value proposition, describe key architectural principles, and navigate the AWS Management Console. This certification establishes credibility in cloud fluency and readiness to work with cloud-based solutions and teams.

Achieved this associate-level certification by demonstrating the ability to design, build, and deploy AI solutions using Microsoft Azure. Developed advanced skills in implementing Azure Cognitive Services, Azure Machine Learning, and other key tools to solve real-world AI problems across computer vision, natural language processing (NLP), speech, and decision-making systems. Gained proficiency in managing the complete machine learning lifecycle, including data ingestion, preprocessing, model training, evaluation, and operationalisation. Built and integrated conversational AI experiences using Azure Bot Services and Language Studio. Emphasised ethical AI practices through responsible AI design, fairness, transparency, and security. This certification confirms readiness to work as part of a cross-functional AI engineering team, building scalable, secure, and responsible AI applications in cloud environments.

Microsoft Certified: Azure AI Fundamentals — Demonstrated foundational expertise in artificial intelligence (AI) and machine learning (ML), with a focus on responsible AI principles and real-world applications using Microsoft Azure. Gained hands-on experience with Azure Cognitive Services, including computer vision (image classification, facial recognition, and OCR), natural language processing (language detection, key phrase extraction, and sentiment analysis), and conversational AI through Azure Bot Services. Developed a strong understanding of AI model lifecycle, from training and evaluation to deployment using Azure Machine Learning. The certification also covered ethical considerations and best practices in building scalable, cloud-based AI solutions. This credential affirms readiness to contribute to AI-driven projects in both development and strategic implementation contexts.