Junior Analyst at World Wireless Solutions, Toronto, ON

Assisted in developing and deploying machine learning models that improved predictive analytics capabilities by 30%. Conducted experiments to fine-tune model performance, ensuring optimal accuracy and scalability. Collaborated with data engineering teams to create data pipelines for AI model training and deployment.

Software Engineer Intern at Bluechip Technologies, Lagos, Nigeria

Led the design and implementation of AI algorithms for a recruitment system, increasing efficiency by 43%. Integrated AI models into existing software applications to enhance decision-making processes. Supported the team with technical research and prototyping of machine learning solutions.

AI Development Team Lead at World Wireless Solutions Inc.

Led AI development with tools like Airflow, Docker, and Kubernetes to build CI/CD pipelines that tripled deployment speed and reduced downtime. Employed explainability techniques such as SHAP and LIME to ensure ethical AI compliance. Automated NLP document classification using PyTorch and AWS SageMaker, increasing accuracy to 96% and saving 50+ hours weekly. Integrated AI workflows with N8N and Zapier to automate data ingestion, cutting manual reporting by 70%.

Junior AI/ML Analyst at World Wireless Solutions

Developed large language model summarizers and retrieval-augmented generation (RAG) applications using LangChain and LangGraph, reducing document review time by 60%. Built scalable data pipelines with Spark and Azure ML, improving ML deployment speed by 40%. Created serverless AI endpoints using AWS Lambda and Bedrock for low-latency inference in customer-facing applications.

Software Engineer Intern at Bluechip Technologies

Automated resume screening using Scikit-learn, improving candidate shortlisting efficiency by 43%. Developed recruitment model prediction APIs with Flask and REST for real-time hiring decisions.

AI Development Team Lead at World Wireless Solutions Inc.

Led AI development projects remotely, building CI/CD pipelines with Airflow, Docker, and Kubernetes that tripled deployment speed and reduced downtime. Implemented model explainability using SHAP and LIME to ensure compliance with ethical AI standards. Automated NLP document classification in PyTorch on AWS SageMaker, achieving 96% accuracy and saving over 50 hours per week. Integrated AI workflows using N8N and Zapier, reducing manual reporting by 70%.

Junior AI/ML Analyst at World Wireless Solutions

Developed large language model summarizers and retrieval-augmented generation (RAG) applications with LangChain and LangGraph, reducing document review time by 60%. Built scalable data pipelines with Spark and Azure ML, improving machine learning deployment speed by 40%. Created serverless AI endpoints using AWS Lambda and Bedrock for low-latency inference powering customer-facing applications.

Freelance Machine Learning, Data Engineering & Business Analyst at Self-employed

Delivered predictive models in Python and R that improved forecasting accuracy by 35%. Designed ETL workflows using SQL, Hadoop, and Spark, reducing pipeline run times by 35%. Built interactive dashboards with Tableau, Power BI, and Alteryx, enhancing reporting efficiency by 50%. Developed NLP sentiment analysis and summarization systems with NLTK, Gensim, Flask, and FastAPI, cutting manual review effort by 40%.

Software Engineer Intern at Bluechip Technologies

Automated resume screening with scikit-learn, increasing candidate shortlisting efficiency by 43%. Built recruitment model prediction APIs using Flask and REST, enabling real-time hiring decisions.

AI Development Team Lead at World Wireless Solutions Inc.

Led AI initiatives from concept to production. Boosted deployment speed 3× by building CI/CD pipelines (Airflow, Docker, Kubernetes). Implemented model explainability (SHAP, LIME) for ethical AI. Automated NLP document classification (PyTorch, SageMaker) achieving 96% accuracy and saving 50+ hours/week. Integrated AI workflows with N8N & Zapier, reducing manual reporting tasks by 70%.

Junior AI/ML Analyst at World Wireless Solutions

Built scalable data pipelines with Spark & Azure ML; improved ML deployment speed by 40%. Developed RAG apps & summarizers (LangChain, Flask); cut document review time by 60%. Created serverless AI endpoints (AWS Lambda, Bedrock) powering customer apps.

Freelance ML & Data Engineering Consultant at Remote

Delivered predictive models in Python/R, improving forecasting accuracy by 35%. Designed ETL workflows (SQL, Hadoop, Spark), reducing pipeline run times by 35%. Built executive dashboards (Tableau, Power BI, Alteryx), increasing reporting efficiency by 50%. Deployed NLP tools (NLTK, Flask) for sentiment analysis, reducing manual review by 40%.

Software Engineer Intern at Bluechip Technologies

Automated resume screening with ML, improving candidate selection efficiency by 43%. Built Flask-based recruitment prediction API enabling real-time hiring decisions.

AI Development Team Lead at World Wireless Solutions Inc.

Led AI development initiatives, boosting deployment speed by building CI/CD pipelines (Airflow, Docker, Kubernetes); ensured ethical AI compliance with model explainability (SHAP, LIME); automated NLP document classification (PyTorch, SageMaker) achieving 96% accuracy and saving 50+ hours per week; integrated AI workflows with N8N & Zapier to cut manual reporting tasks by 70%.

Junior AI/ML Analyst at World Wireless Solutions

Built scalable data pipelines with Spark & Azure ML, improving ML deployment speed by 40%; developed RAG apps & summarizers (LangChain, Flask) reducing document review time by 60%; created serverless AI endpoints (AWS Lambda, Bedrock) powering customer apps.

Freelance ML & Data Engineering Consultant at Remote

Delivered predictive models in Python/R, improving forecasting accuracy by 35%; designed ETL workflows (SQL, Hadoop, Spark), reducing pipeline run times by 35%; built executive dashboards (Tableau, Power BI, Alteryx) increasing reporting efficiency by 50%; deployed NLP tools (NLTK, Flask) for sentiment analysis, reducing manual review by 40%.