Backend Engineer
Mobolaji F. is a results-driven Machine Learning, AI, and MLOps engineer with 4+ years of experience designing, building, and deploying end-to-end ML solutions that reduce manual reporting by 85%, boost model accuracy by 20%+, and cut deployment times by 60%, with proven impact on user engagement, retention, and signups; he excels in data preprocessing, feature engineering, model training, hyperparameter optimization, predictive analytics, statistical analysis, data analytics, data visualization, data modeling, deep…
Great score on Implement an LRU Cache Algorithm, Difficulty Medium · Great score on Find the Length of the Longest Substring Without Repeating Characters, Difficulty Medium · Great score on Understanding Rate Limiting in APIs, Difficulty Medium · Perfect score on Ensuring Real-time Data Accuracy in Mobile Applications, Difficulty Medium
MLOps and AI Solutions Specialist
10AlyticsOct 2022 — Sep 2025- Designed and implemented scalable, end-to-end machine learning pipelines for classification and regression tasks, which led to an 85% reduction in manual reporting and improved decision-making across multiple departments.
- Performed in-depth data cleaning, feature extraction, and transformation on large datasets using Pandas, NumPy, and scikit-learn to ensure model readiness and boost model accuracy by over 20%.
- Developed and deployed ML models as RESTful APIs using FastAPI, enabling real-time prediction capabilities that integrated seamlessly with client-facing web apps and internal dashboards.
- Containerized the entire application using Docker and deployed to Azure, ensuring consistent behavior across development and production environments and reducing deployment time by 60%.
PythonDockerAzure Ai FoundryAzure Machine learningSQLPower BIFull Stack Data Scientist
Simplified UX – Chicago, USA (Remote)Mar 2020 — Jul 2022- Led development of Power BI dashboards, visualizing user activities and subscription trends. Utilized MongoDB query language for efficient data retrieval. Integrated real-time data feeds for up-to-date insights. This resulted in a 20% increase in user engagement and a 15% rise in subscription renewals, driving company growth.
- Utilized machine learning algorithms to predict user behavior especially customer churn on the platform. Analyzed historical data to identify patterns and trends. Implemented personalized recommendations resulting in a 25% increase in user engagement and higher subscription retention rates, contributing to the company's growth in the competitive market.
- Facilitated regular cross-functional meetings, notably with the social media manager, to align strategies. Presented Power BI insights on user retention, signup trends, and campaign impacts. Collaborative efforts resulted in a significant 25% increase in new signups, effectively enhancing overall customer engagement and user retention.
PythonMongoDBPower BIHTMLCSSJavaScript
Bachelor of Science
University of IlorinIlorin, Kwara, Nigeria 2016 — 2021
Backend Engineer
Mobolaji is available for hire
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Mobolaji F. is a results-driven Machine Learning, AI, and MLOps engineer with 4+ years of experience designing, building, and deploying end-to-end ML solutions that reduce manual reporting by 85%, boost model accuracy by 20%+, and cut deployment times by 60%, with proven impact on user engagement, retention, and signups; he excels in data preprocessing, feature engineering, model training, hyperparameter optimization, predictive analytics, statistical analysis, data analytics, data visualization, data modeling, deep…
- Projects completed1
- Hourly rate
- 3-4 years experience
- Member since Oct 2025
Great score on Implement an LRU Cache Algorithm, Difficulty Medium · Great score on Find the Length of the Longest Substring Without Repeating Characters, Difficulty Medium · Great score on Understanding Rate Limiting in APIs, Difficulty Medium · Perfect score on Ensuring Real-time Data Accuracy in Mobile Applications, Difficulty Medium
MLOps and AI Solutions Specialist
10AlyticsOct 2022 — Sep 2025- Designed and implemented scalable, end-to-end machine learning pipelines for classification and regression tasks, which led to an 85% reduction in manual reporting and improved decision-making across multiple departments.
- Performed in-depth data cleaning, feature extraction, and transformation on large datasets using Pandas, NumPy, and scikit-learn to ensure model readiness and boost model accuracy by over 20%.
- Developed and deployed ML models as RESTful APIs using FastAPI, enabling real-time prediction capabilities that integrated seamlessly with client-facing web apps and internal dashboards.
- Containerized the entire application using Docker and deployed to Azure, ensuring consistent behavior across development and production environments and reducing deployment time by 60%.
PythonDockerAzure Ai FoundryAzure Machine learningSQLPower BIFull Stack Data Scientist
Simplified UX – Chicago, USA (Remote)Mar 2020 — Jul 2022- Led development of Power BI dashboards, visualizing user activities and subscription trends. Utilized MongoDB query language for efficient data retrieval. Integrated real-time data feeds for up-to-date insights. This resulted in a 20% increase in user engagement and a 15% rise in subscription renewals, driving company growth.
- Utilized machine learning algorithms to predict user behavior especially customer churn on the platform. Analyzed historical data to identify patterns and trends. Implemented personalized recommendations resulting in a 25% increase in user engagement and higher subscription retention rates, contributing to the company's growth in the competitive market.
- Facilitated regular cross-functional meetings, notably with the social media manager, to align strategies. Presented Power BI insights on user retention, signup trends, and campaign impacts. Collaborative efforts resulted in a significant 25% increase in new signups, effectively enhancing overall customer engagement and user retention.
PythonMongoDBPower BIHTMLCSSJavaScript
Bachelor of Science
University of IlorinIlorin, Kwara, Nigeria 2016 — 2021
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