Kenya
โWith great power comes great responsibility.โ With extensive experience in machine learning, software engineering, and distributed systems, I specialize in building scalable machine learning infrastructure, real-time data pipelines, and high-performance backend systems. I have led and contributed to projects supporting large-scale financial platforms, processing thousands of transactions per second and serving millions of users. My expertise spans machine learning engineering, MLOps, cloud computing, and system architecture, and I have successfully delivered solutions in multinational, remote, and cross-functional environments, consistently improving operational efficiency and system performance. ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐จ๐ง๐๐ฅ ๐๐ฎ๐ฆ๐ฆ๐๐ซ๐ฒ In previous roles across fintech, machine learning engineering, and software development, I have led and collaborated with multidisciplinary teams to design, deploy, and maintain scalable systems and machine learning models in production environments. I have developed technical strategies, automation workflows, and communication frameworks that aligned engineering execution with organizational goals, improving deployment speed, system reliability, and data-driven decision-making across projects and platforms. ๐๐๐ฒ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ Machine Learning, Deep Learning, MLOps, Distributed Systems, Data Pipelines, Model Deployment, Real-time Processing, Cloud Computing, Backend Development, API Development, System Architecture, Model Optimisation, Predictive Modelling, NLP & LLMs, CI/CD, Containerisation, Kubernetes. ๐๐ข๐ ๐ง๐ข๐๐ข๐๐๐ง๐ญ ๐๐๐ก๐ข๐๐ฏ๐๐ฆ๐๐ง๐ญ๐ฌ โข Built real-time ML pipelines processing 5,000+ transactions per second supporting large-scale financial systems. โข Developed optimisation systems that reduced operational costs by up to 30% for multiple clients. โข Implemented MLOps pipelines that reduced model deployment time by 80% and improved system reliability. ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐จ๐ง๐๐ฅ ๐๐ก๐ข๐ฅ๐จ๐ฌ๐จ๐ฉ๐ก๐ฒ ๐๐ง๐ ๐๐จ๐๐ฅ๐ฌ I am passionate about building intelligent systems that solve real-world problems at scale. My goal is to continue advancing in machine learning infrastructure and AI engineering, contributing to innovative, high-impact systems while continuously improving my skills in large-scale data systems, AI deployment, and distributed computing. I am always open to connecting with professionals, collaborating on innovative projects, and exploring new opportunities in machine learning, AI, and software engineering. Feel free to reach out to discuss technology, data, or potential collaborations.
At Safaricom PLC, I work at the intersection of machine learning, data engineering, and production systems, focusing on deploying scalable ML solutions that support millions of customers and core business operations. My role involves building real-time data pipelines, developing automated MLOps workflows, and deploying machine learning models into production environments with high availability and low latency. I collaborate with data scientists, analysts, and engineering teams to transform research models into production-ready systems while improving deployment efficiency, monitoring, and retraining processes. I have also contributed to integrating LLM and RAG-based solutions into internal tools, improving feature discovery and experimentation workflows. Through this work, I help drive the organizationโs transition toward scalable, automated, and production-focused machine learning systems. Key Achievements 1. Architected real-time ML pipelines processing over 5,000 transactions per second, enabling near real-time decision-making and improved operational efficiency. 2. Implemented automated MLOps workflows that reduced data processing and deployment turnaround time by 30%. 3. Integrated LLM and RAG-based intelligent retrieval systems into internal tools, improving feature discovery across a 10,000+ feature store. 4. Deployed scalable ML infrastructure serving millions of users with high uptime and low-latency inference.
Contributing to global open-source projects has allowed me to drive impactful software solutions used by thousands of users worldwide. My role focuses on designing, developing, and maintaining scalable software modules while collaborating with distributed contributors across multiple time zones. I lead efforts to optimize codebases, implement CI/CD workflows, and ensure consistent project quality through asynchronous code reviews. Beyond coding, I mentor new contributors, author technical documentation, and enhance community engagement. By resolving critical bugs and implementing feature improvements, I help projects iterate faster, maintain long-term sustainability, and deliver reliable, high-performance solutions to a global audience. Key Achievements 1. Developed and optimized software modules, improving system performance and functionality for thousands of global users. 2. Implemented CI/CD workflows and asynchronous code reviews, ensuring consistent quality across distributed contributions. 3. Mentored new contributors and created technical documentation, increasing repository adoption and engagement by 15%. 4. Refactored codebases and performed performance tuning, reducing runtime errors and improving efficiency by 20%.
I partner with clients across fintech, e-commerce, and ad tech to deliver end-to-end ML solutions that drive measurable business impact. My work spans the full lifecycleโfrom data preprocessing and model development to production deployment and lifecycle managementโensuring reliability, scalability, and performance. I design optimization algorithms and NLP/LLM-based systems that reduce operational costs, predict customer behavior, and automate decision-making processes. Collaborating with international clients in remote-first environments, I translate complex business problems into actionable ML solutions while implementing robust MLOps pipelines, automated retraining, and monitoring workflows. Freelancing has honed my ability to think beyond code, balancing technical innovation with tangible business outcomes. Key Achievements 1. Developed and deployed end-to-end ML models and pipelines for global clients, improving operational efficiency and decision-making. 2. Designed optimization algorithms that reduced client operational costs by up to 30% through data-driven resource allocation. 3. Built scalable NLP and LLM-based solutions for content classification and customer intent prediction, handling thousands of daily requests. 4. Implemented automated MLOps workflows reducing model deployment time by 80% and improving reliability with CI/CD, Docker, and Kubernetes.
At Huawei Technologies, I contributed to the design and deployment of scalable fintech solutions powering mobile money and digital finance platforms, including VOOMA, M-PESA, Cashia, and ZIDII, serving over 30 million users and billions of transactions. My role combined software engineering, system architecture, and cross-functional collaboration to ensure high-performance, reliable, and secure platforms. I led efforts to standardize code, create detailed technical documentation, and develop robust architectural patterns that reduced integration errors and improved system efficiency. By implementing automated build pipelines, acceptance testing plans, and integration workflows, I streamlined deployment processes while mentoring team members to accelerate onboarding and maintain high-quality delivery. This experience honed my ability to translate complex business requirements into technically sound, large-scale fintech systems. Key Achievements 1. Designed and deployed scalable fintech solutions supporting 30M+ users and billions of transactions, ensuring high system availability and reliability. 2. Implemented comprehensive Acceptance Test Plans, improving project validation efficiency by 25% and reducing deployment errors. 3. Introduced robust architectural patterns, coding standards, and technical documentation, reducing system integration errors by 20%. 4. Authored functional and configuration specifications (FRS) for 10+ major system functionalities, aligning technical execution with business requirements.
This role introduced me to the critical demands of production reliability at scale. I focused on maintaining high-availability financial systems, including Safaricomโs Fuliza (Overdraft) and M-PESA platforms, ensuring near-perfect uptime while supporting millions of daily transactions. Beyond keeping systems running, I leveraged data analytics to generate actionable business insights for Fuliza, contributing to operational decisions and product improvements. Working at the intersection of infrastructure, monitoring, and analytics, I developed a deep understanding of production-grade reliability, process optimization, and proactive incident management in high-stakes financial services. Key Achievements 1. Maintained Fuliza and M-PESA systems with 99.9999% uptime, supporting millions of daily transactions without disruption. 2. Generated actionable business insights for Fuliza using data analytics, informing strategic product and operational decisions. 3. Developed monitoring and alerting workflows to proactively detect and resolve potential system issues. 4. Strengthened incident response protocols, improving resolution times and operational resilience.
At Zuyu, I contributed to building secure and scalable backend systems for an early-stage MVP, where every line of code directly impacted the product and user experience. Working in a fast-paced startup environment, I collaborated closely with the frontend team to ensure seamless integration, while designing systems that prioritized security, reliability, and performance. From developing authentication APIs to implementing role-based access control, I helped lay the foundation for a secure, maintainable backend that supports rapid product iteration and growth. Key Achievements 1. Developed secure authentication APIs using Node.js, Express, and TypeScript, enabling reliable user access management. 2. Designed and implemented a Role-Based Access Control (RBAC) system to strengthen backend security and manage user permissions. 3. Collaborated closely with the frontend team to ensure smooth integration and reliable data flow between frontend and backend systems. 4. Contributed to building the MVP infrastructure, supporting fast product iterations in a startup environment.