Resume

Design and deploy applied ML models using PyTorch, scikit-learn, and Hugging Face for predictive maintenance, anomaly detection, and operational optimization.

Architect RAG pipelines using Milvus, Ollama, vLLM, and Lucid to support dynamic agents for contextual business queries.

Connect and orchestrate industrial data sources using MQTT, Kepware, and integrate with legacy AS400/DB2 for i systems via 5250 terminal sessions.

Containerize and optimize AI services using Podman, OpenBLAS, and IBM Power systems (ppc64le), enabling high-performance deployments at the edge and in hybrid infrastructures.

Lead orchestration of intelligent agents and toolchains using AutoGen and BeeAI, integrating with Trello, MES systems, planning tools, and cloud storage platforms.

Collaborated with Essist Omikron and IBM iUG to architect and implement AI and IIoT solutions tailored for enterprise environments.

Leveraged IBM Cloud services to integrate AI and IIoT technologies into existing infrastructure, optimizing operational efficiency and scalability for industrial applications.

Drove innovation in enterprise IT systems by modernizing IBM i system management, AI deployments, and IIoT solutions for smarter, connected operations.

Active contributor to user groups and events, sharing insights on AI deployment, IIoT integration, and cloud technologies within IBM ecosystems.

Spearheaded the integration of TensorFlow and NLP models with Node-RED, enhancing automation and efficiency in data analysis processes.

Innovated and implemented advanced NLP algorithms, improving ML model accuracy and contributing to groundbreaking research in machine learning integration.

Led the Multiply Project as Practice Educator & Team Lead, managing and mentoring new tutors and providing one-to-one online tuition to adult learners via CANVAS.

Participated in the development of educational resources and contributed to outreach and recruitment activities in the community.

Engineered a sophisticated market basket analysis using Python and the Apriori algorithm, analysing over 500,000 transaction datasets to uncover product associations, increasing cross-selling strategies by 20%.

Engineered a machine learning model that categorized customer sentiment with 95% accuracy, leading to a 30% improvement in targeted marketing strategies and customer satisfaction rates.

Analyzed over 5TB of structured and unstructured data using advanced machine learning algorithms in Python, enhancing predictive models and leading to a 20% increase in decision-making efficiency.

Developed a machine learning model that analyzed and predicted customer behavior with 95% accuracy, leveraging Python, R, and TensorFlow to parse through 10TB of user data.

Engineered a machine learning model to categorize customer feedback with a 95% accuracy rate, streamlining the decision-making process and increasing response efficiency by 30%.

Integrated LangChain and Llama technologies to develop an advanced chatbot prototype, resulting in a 50% improvement in natural language understanding and response accuracy.

Conducted comprehensive data quality audits by evaluating metrics, utilizing pivot tables to identify and report anomalies, leading to a significant improvement in overall data quality.

Developed Tableau dashboards to track transactions and revenue, identifying the most profitable customers and contributing to a 15% increase in customer retention.

Analyzed large data sets to pinpoint trends and anomalies, presenting insights in detailed reports and presentations to stakeholders, enhancing data-driven decision-making.