United Kingdom
I am a software engineer specialising in AIOps, intelligent automation, agentic AI systems, and cloud-native engineering on Oracle Cloud Infrastructure (OCI). As a Certified Oracle Cloud Infrastructure Generative AI Professional (2024, OCP), I build advanced operational frameworks that integrate Generative AI, LLM orchestration, multi-agent architectures, and vector databases to deliver predictive, autonomous, and self-optimising enterprise systems. At the core of my work is CrewAI, which I use to design multi-agent, agentic automation workflows that coordinate reasoning, decision-making, and action across complex operational environments. CrewAI enables me to construct highly flexible, intelligent agent systems that support event-driven automation, autonomous remediation, operational intelligence, and self-healing architectures—all central to modern AIOps strategy. I have hands-on experience across a broad range of OCI services, including OCI Generative AI APIs, OCI Data Science, Logging, Logging Analytics, Events, Monitoring, Alarms, Streaming, and Oracle DevOps. This allows me to build end-to-end, automation-first AIOps pipelines with deep observability engineering, predictive analytics, and automated diagnostics. My engineering background spans software engineering, distributed systems, microservices, DevOps/DevSecOps, CI/CD, API integration, Infrastructure as Code (IaC), and reliability engineering (SRE). I also work extensively with LangChain, Semantic Kernel, and other orchestration frameworks to enhance agentic workflows and integrate LLM agents directly into enterprise operations. These tools enable seamless interoperability between generative AI models, operational data streams, and cloud-native automation components. In addition to my technical experience, I bring a strong background in technical training, teaching, mentoring, and knowledge transfer, supporting teams in adopting AI, cloud, and automation technologies effectively. I thrive in environments that prioritise innovation, continuous improvement, and the evolution of intelligent, autonomous operations. Highly motivated and committed to professional excellence, I am passionate about advancing the practical use of CrewAI, agentic AI, OCI Generative AI, LLM orchestration, event-driven automation, and self-healing operational systems to accelerate AIOps maturity and transform enterprise operations.
Software engineer specialising in AIOps, intelligent automation, agentic AI systems and cloud-native engineering on Oracle Cloud Infrastructure (OCI) and Amazon Web Services (AWS). Certified Oracle Cloud Infrastructure Generative AI Professional (2024, OCP) responsible for building and deploying generative AI and data science solutions using OCI and AWS platforms, including PartyRock, SageMaker and Bedrock. Experienced in developing end-to-end AI pipelines, enterprise automation frameworks and multi-agent operational systems. A key focus of my work is CrewAI. I use CrewAI to design and orchestrate agentic, multi-agent automation workflows that support autonomous remediation, event-driven operations, predictive analytics, operational intelligence and self-healing behaviours. CrewAI also enables integration with LangChain, Semantic Kernel, vector databases and LLM orchestration frameworks to improve AI-driven decision-making. Skilled in using Oracle AI Services, OCI Generative AI APIs, OCI Data Science, Logging, Logging Analytics, Events, Monitoring, Alarms, Streaming, Oracle DevOps and Autonomous Database to build enterprise-grade AIOps pipelines. Experienced with AWS PartyRock for rapid prototyping, SageMaker for model development and Tableau for visualising AI-driven insights. Combines teaching and training experience up to degree level in computer science and software engineering with hands-on experience designing and developing applications for mobile and fixed platforms. Strong communicator skilled in mentoring, knowledge transfer and supporting teams adopting AI and cloud technologies. Technical strengths include Python, natural language processing, large language models, distributed systems, microservices, DevOps, CI/CD, API integration, cloud-native design, Infrastructure as Code and building scalable AI systems across Oracle and AWS. Committed to innovation, improvement and advancing intelligent, autonomous operations.
As an ICT Secondary School Teacher at St David’s High School, I taught Artificial Intelligence (AI), programming, software applications, hardware fundamentals, and core ICT concepts. I planned and delivered curriculum-aligned lessons, adapting teaching methods to support different learning styles while ensuring students engaged with current and emerging digital technologies. I assessed student progress, provided constructive feedback, and offered tailored support to meet individual needs. I incorporated new technologies and developments in ICT into the curriculum, fostering curiosity, computational thinking, and problem solving skills that reflect the foundations of AIOps, automation, and data driven decision making. I maintained an inclusive and well managed classroom environment, complied with safeguarding requirements, and communicated effectively with parents and guardians through accurate records and regular updates. Collaboration with colleagues strengthened interdisciplinary learning opportunities and supported continuous improvement in teaching practice. This role enhanced my ability to explain complex technical concepts, analyse learner data, and manage structured processes transferable skills that align closely with modern AIOps and intelligent operations environments.
During my time as a Trainee Teacher, I engaged in delivering and exploring a range of technology-driven topics, with a strong focus on Artificial Intelligence, Machine Learning, and data-driven automation. I researched the application of AI to improve customer and operational experiences, including work related to AI for Expert Banking Systems, where I applied Python, neural networks, and automation techniques to enhance fraud detection processes and streamline communication workflows. I used case study analysis to investigate robotics and automation, supporting my broader research into real-world AI applications. As part of my teaching practice, I delivered presentations on Machine Learning, demonstrating pattern recognition and image classification using Artificial Neural Networks (ANN) and showcasing foundational concepts used in AIOps and intelligent automation. I employed Python-based data analysis and AI methods to create educational materials capable of identifying patterns, supporting decision-making, and demonstrating autonomous machine learning processes mirroring the principles used in modern AIOps systems. Additionally, I explored AI-driven data mining techniques, applying the K-Nearest Neighbour (KNN) algorithm for regression, classification, and anomaly detection tasks. This experience reflects my commitment to integrating AI, ML, automation, and data analytics into practical learning environments while developing skills transferable to AIOps, data science, and ML-driven operational roles.
I began my career as a Software Engineer, developing mobile, web, and fixed-platform applications using 3-tier, MVC, and MVVM architectures. I supported the full software development lifecycle, from requirements analysis and coding through to testing and deployment, gaining a strong foundation in structured engineering and system reliability. As I progressed into Software Engineer and Senior Software Engineer roles, I contributed to increasingly complex projects across Android, iOS, and web environments. I worked with Java, Python, C/C++, C#, JavaScript, SQL, Spring, and .NET, and developed applications using Android Studio, Xcode, App Inventor, Ionic, and Titanium. I applied Agile practices, black-box testing, and functional validation to ensure high-quality delivery and improved system performance. I later transitioned into a Software Architect position, where I designed scalable, distributed systems based on microservice architectures and incorporated DevOps and CI/CD practices to streamline release processes. I produced high-level and detailed architectural designs using UML, ERD, ELH, and DFD, and developed interoperability frameworks to support seamless communication between services. My work also involved white-box integration testing and the introduction of cloud-ready design principles. During the final phase of the role, I expanded into artificial intelligence, machine learning, and data analytics, integrating ML models into operational workflows and developing Python/SQL pipelines for predictive analytics, anomaly detection, and automated decision-making. This work aligned with the principles of AIOps, including intelligent automation, operational analytics, event correlation, and data-driven optimisation. I left the role, having developed comprehensive experience across software engineering, architecture, automation, and ML integration, preparing me strongly for modern AIOps and intelligent operations roles.