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Akano Wisdom

@akanowisdom

Senior Data Scientist and AI Engineer building production ML and LLM/RAG systems that improve accuracy, latency, and outcomes.

Nigeria

Message

What I'm looking for

I’m looking for fully remote, global opportunities where I can own end-to-end ML and LLM/RAG products on AWS—shipping measurable gains in accuracy and latency through strong MLOps and production-grade pipelines.

I’m a results-driven Data Scientist and AI Engineer with 4+ years of production experience delivering end-to-end machine learning—from feature engineering and model development to MLOps, CI/CD, and cloud deployment on AWS.

I’ve shipped measurable improvements across real systems, including 40% better prediction accuracy, 65% lower query execution time, and data pipelines cut from 8 hours to 30 minutes—turning models into decisions people can act on.

My specialty is LLM-powered applications and RAG systems. In recent work, I led AI architecture for a production platform monitoring 774 LGAs using ML dropout prediction and a multilingual WhatsApp RAG chatbot, and I’ve deployed Llama 2 fine-tuning and source-grounded RAG to reduce hallucinations.

Experience

Work history, roles, and key accomplishments

Current

AI Lead

Current

Kotlead Children's Tech Education Foundation

Jun 2025 - Present (1 year)

Led AI architecture and development of EduSentinel, achieving ROC-AUC 0.89 for ML dropout prediction and deploying a multilingual (5-language) WhatsApp RAG chatbot for at-risk out-of-school children. Built an end-to-end pipeline with XGBoost/LightGBM, SHAP explainability, automated MLflow retraining, and a FastAPI/Streamlit stack deployed via Docker Compose.

XGBoost LightGBM SHAP MLFlow fastAPI Streamlit Docker Compose

Current

Data Scientist / AI Engineer

Current

Kotlead

Jan 2023 - Present (3 years 5 months)

Built and deployed end-to-end ML models on AWS, improving customer behaviour prediction accuracy by 40% and reducing model drift by 25% with automated retraining. Designed AWS data pipelines for 1M+ daily records (Lambda/S3/CloudWatch), cutting processing time from 8 hours to 30 minutes, and fine-tuned Llama 2 7B for finance expertise with SageMaker plus source-grounded RAG to reduce hallucination

AWS Sagemaker Pinecone SQL Optimization Recommendation Systems AWS Cloudwatch MLFlow Docker

AI Business Intelligence Analyst

GRITO Talent Agency

Jun 2023 - Mar 2024 (9 months)

Engineered ML models for customer behaviour analysis across the Grito.africa platform, improving accuracy by 40% and deploying recommendation systems for 5,000+ daily active users. Delivered business intelligence insights for senior leadership while implementing automated retraining pipelines that reduced model drift by 25%.

Machine Learning Recommendation Systems SQL Analytics Data Pipelines Business Intelligence