Hire the Best Machine Learning Engineers
in Nigeria

Hi! I'm Enoch — an AI System Engineer and SaaS Backend Specialist helping businesses automate complex workflows, integrate intelligent AI models, and scale high-performance systems. I build custom AI products, automated SaaS backends, and fault-tolerant pipelines that help tech companies scale faster and eliminate operational overhead. I partner with SaaS companies and growth-stage organizations to bridge the gap between complex machine learning models and production-grade software—turning raw AI capabilities into fast, zero-downtime systems. Proven Technical Impact & Results 100% Uptime Audio Infrastructure: Architected multi-lingual Text-to-Speech backends (FastAPI, OpenAI, Meta MMS, YarnGPT) featuring automated offline failover systems to guarantee zero service downtime. 80% Operational Time Savings: Engineered real-time predictive monitoring and automated workflow pipelines using FastAPI, Supabase, and n8n. 40% Faster Release Cycles: Automated predictive ML pipelines and CI/CD workflows on Azure, accelerating production release speed without user disruption. 30% Faster Data Analysis: Built domain-specific AI chatbots and multi-tenant RAG architectures (GPT-4o-mini, Pinecone, PostgreSQL) to automate complex data retrieval. Core Stack & Tools AI & Machine Learning: PyTorch, OpenAI API, Meta MMS, YarnGPT, RAG, Pinecone, LangChain Backend & SaaS: Python, FastAPI, Async I/O, Pydantic, REST APIs, PostgreSQL, Supabase Automation & DevOps: n8n, Azure ML, GCP, Docker, CI/CD Pipelines Let's Build Your System Whether you need a custom multi-lingual voice engine, a secure RAG chatbot, or an automated backend pipeline, I build production-ready software designed to scale.

I build machine learning systems that work in production and explain their own decisions -- a combination that most data scientists cannot offer and that regulated industries legally require. Two recent examples of what this looks like in practice: CreditIQ -- a loan default prediction system built on 150,000 real borrower records using XGBoost and SHAP explainability. The system achieved AUC-ROC of 0.844 and includes business cost threshold optimisation -- finding the exact decision point that minimises total dollar loss for the lender rather than just maximising statistical metrics. Live and accessible right now. MacroSense -- a US economic forecasting system that predicts GDP growth, inflation, and unemployment 6 months ahead using Federal Reserve data. Walk-forward validation across 25 years of economic history confirmed 88.9% directional accuracy for GDP forecasting. Also live and accessible. Both systems are deployed as interactive dashboards that non-technical stakeholders can use without any data science knowledge. Both include full SHAP explainability documentation. Both have clean reproducible codebases on GitHub. --- WHAT SEPARATES MY WORK FROM GENERIC ML FREELANCERS: Most freelancers deliver a Jupyter notebook with good accuracy metrics and call it done. I deliver systems -- with deployment, documentation, interpretability, and honest evaluation on data the model has never seen. I also have something most ML engineers do not -- an economics degree and published research in macroeconomic modelling. This means when I build a credit risk model or a financial forecasting system I understand the domain behind the data not just the algorithms processing it. That understanding prevents the kind of technically correct but economically nonsensical predictions that make clients distrust their own models. --- WHAT I BUILD: Predictive ML Systems Credit risk scoring, loan default prediction, churn prediction, revenue forecasting -- classification and regression problems on structured financial and economic data using Python, XGBoost, scikit-learn, and Random Forest. Time Series Forecasting Economic indicator forecasting, demand forecasting, financial time series analysis using ARIMA, GARCH, XGBoost, and ensemble methods. Validated using walk-forward methodology -- the honest standard for time series evaluation. Explainable AI Systems SHAP-powered model interpretation for regulated industries where "the algorithm said so" is not an acceptable answer. Every prediction comes with an auditable explanation showing exactly which factors drove the decision. Econometric Modelling OLS regression, ARDL, cointegration analysis, ADF stationarity testing, and econometric specification for research and policy analysis. Published researcher with two papers submitted to peer-reviewed journals. End-to-End Deployment Streamlit interactive dashboards, FastAPI prediction endpoints, GitHub repositories with professional documentation. I deliver something clients can actually use -- not something that only works on my laptop. WHO I WORK BEST WITH: Fintech companies and lending institutions that need credit risk or fraud detection models with regulatory-grade explainability. Banks and financial services firms needing economic forecasting or market risk models. Research institutions and consultancies needing rigorous quantitative analysis combining econometric and ML approaches. Startups that need production-quality ML systems built properly the first time rather than rebuilt six months later. --- WHO I DO NOT WORK WITH: Clients who need work done in 24 hours regardless of quality. Good ML systems require proper validation. Clients who want me to fabricate or misrepresent model performance. I document limitations as thoroughly as I document results. --- If your project involves structured data, financial or economic modelling, or requires a system that can explain its own predictions to regulators and stakeholders -- let us talk. I respond to all messages within 24 hours and provide a free 15-minute consultation call for any project above $1000. SKILLS #Machine Learning #Python #Data Science #XGBoost #Time Series Analysis #Credit Risk #scikit-learn #Statistical Analysis #Financial Analysis #Data Visualization #Feature Engineering #Predictive Modeling #SHAP / Explainable AI #Fraud Detection #Economic Forecasting #Econometrics #pandas 18. NumPy #Streamlit #SQL #Deep Learning #Natural Language Processing #GARCH Models #ARIMA #Regression Analysis #Classification #Random Forest #Data Cleaning #API Integration #Git / GitHub

Machine Learning Engineer with solid expertise in Python programming, Artificial Neural Networks, Convolutional Neural Networks, Natural Language Processing, and Computer Vision. I work extensively with Large Language Models (LLMs), prompt engineering, and AI agents, combining deep learning techniques with real-world applications across vision and language. Passionate about building scalable, intelligent systems that bridge the gap between perception and reasoning.

I build machine learning models that go into production, not just notebooks and I have led enterprise ML product delivery for real companies, not just built proof-of-concepts. WHAT I BUIID: ✅ Machine Learning Pipelines & MLOps 1. Sales & Demand Forecasting (recent build: 90% accuracy). 2. Churn Prediction, deployed as a live API (85% accuracy). 3. Risk Modeling & Customer Segmentation. 4. Recommender Systems. 5. Model serving with FastAPI, containerized with Docker, deployed on Azure. 6. Experiment tracking with MLflow, CI/CD with GitHub Actions, models that are deployed and monitored, not just accurate on a test set. ✅ Enterprise-Grade Delivery Experience 1. Founded and led the AI & Data Innovation department at Snapnet, directing enterprise product delivery including AcumeetAI, ProcureCentro, and HCMatrix. 2. A rare combination for a freelancer: hands-on ML engineering plus experience shipping products at an organizational level , architecture decisions through stakeholder buy-in. HOW I WORK: 1. Clear milestones, not open-ended scope. 2. Progress updates with technical specifics, not vague status reports. 3. Upfront early if I spot a scoping issue, rather than letting it surface mid-project. If you need a model that performs and holds up in production, backed by real MLOps practice and enterprise-grade thinking, let's talk.

Hi, I'm Wale, a Mobile App Development Expert. I build cross-platform mobile apps using FlutterFlow, Flutter, and React native, with Firebase, Supabase or custom backends. I deliver clean, fast, and launch-ready MVPs, AI-powered apps, SaaS products, subscription platforms, and full-scale applications that run natively on iOS and Android platforms. I work directly with startup founders, non-technical entrepreneurs, and product teams that are ready to build applications that users would actually use. I've built and delivered apps across food delivery, music streaming, multiplayer gaming, sports, social platforms, AI tooling, and SaaS products; handling everything from UI through backend architecture to App Store and Google Play submission. MY DEVELOPMENT METHODOLOGY 🚀 Scope Before Build — Every engagement starts with a precise scope agreement. Before anything is built, I sit with you and use my experience across dozens of shipped apps to help you see your own idea more clearly. By the time we finish this conversation, you will understand your own project better than you did when we started, and we will both know exactly what is being built. 🚀 Core-First Delivery — I build the feature that proves your concept works before anything else. The version that validates your idea ships first. Everything else builds on top of a working foundation. 🚀 Real-Device Testing — Every app is tested on actual iOS and Android hardware before delivery. Not simulators. Not assumptions. 🚀 Clean Handover — You receive a fully organized FlutterFlow project, backend access, and documentation, all connected to your accounts. Complete ownership with nothing held back. TOOLS & SERVICES 🚀 Platforms & Frameworks - FlutterFlow · Flutter · React.js · Firebase Firestore · Supabase · Firebase Authentication · Firebase Cloud Functions · Google Cloud · Hygraph CMS 🚀 Integrations - OpenAI API · Claude API · Google Gemini · Google Vision · Google Cloud TTS · ElevenLabs · RevenueCat · Stripe · Paystack · REST APIs 🚀 Delivery - iOS TestFlight · App Store Submission · Google Play Console · Google Play Internal Testing 🚀Services - Cross-Platform Mobile App Development · MVP Development · AI-Powered App Development · SaaS Mobile Applications · Subscription & Payment Integration · Firebase Backend Architecture · Supabase Backend Setup · FlutterFlow Bug Fixes & Project Rescue · App Performance Optimization FREQUENTLY ASKED QUESTIONS Q: Have you actually deployed apps to the Apple App Store and Google Play Store? Yes. I have taken apps through the full submission pipeline on both platforms. From provisioning profiles, bundle IDs, and App Store Connect configuration on the iOS side, to Google Play Console setup, signed APK generation, and internal testing tracks on Android. Q: How long does a FlutterFlow mobile app project typically take from start to delivery? A focused MVP with 4 to 6 screens, authentication, and a live Firebase or Supabase backend typically takes 1 to 2 weeks. Apps with AI integrations, payment flows, real-time multiplayer, or complex backend logic take 3 to 6 weeks depending on scope. I give you a precise timeline during our scoping conversation. Q: Will I fully own the app and backend after the project is complete? Yes, you own every source code, flutterflow project, Firebase or Supabase backend, all API connections, and every asset delivered are linked to your accounts, not mine. You own everything completely from day one. I do not retain access, hold dependencies, or lock you into any ongoing arrangement unless you specifically want one. Q: Can you integrate AI features like ChatGPT, Claude, or Gemini into a FlutterFlow app? Yes. AI integration is one of the fastest-growing areas of my work. I connect FlutterFlow apps to OpenAI, Anthropic Claude, Google Gemini, Google Vision for OCR, Google Cloud TTS for voice output, and ElevenLabs for voice cloning. Chatbots, document processing, speech-to-text, AI-generated content, and intelligent recommendation systems are all within scope. Q: I have an existing FlutterFlow project that is broken or incomplete, can you fix it? Project rescues are a regular and significant part of my work. Send me access to the FlutterFlow project and describe what is broken, missing, or stuck. I will review the codebase, diagnose the root cause, and give you an honest written assessment of what it will take to complete or repair it before any commitment is made.

Hi, I'm Umar — a quant-focused, business-driven Data Scientist and Data Analyst with 3+ years of experience helping businesses and traders turn complex data into actionable insights, predictive models, automated analytics, and quantitative trading solutions. If you have a messy dataset, a business question that needs answering, a dashboard that needs building, or a trading strategy that needs testing, I can help take it from raw data to a reliable, usable solution. I specialize in: Data Science & Machine Learning Predictive Modeling — Classification, Regression, Clustering Predictive Analytics & Forecasting Time Series Forecasting Feature Engineering & Model Optimization Statistical Analysis & Hypothesis Testing NLP & Sentiment Analysis Model Evaluation & Explainable AI Python, Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, TensorFlow Data Analytics & Business Intelligence Data Analysis & Exploratory Data Analysis (EDA) SQL Data Extraction, Transformation & Analysis Data Cleaning, Validation & Preparation Power BI & Tableau Dashboards Microsoft Excel Analytics & Reporting Automation KPI Analysis & Business Reporting Data Visualization Customer, Sales & Operational Analytics ETL & Automated Data Pipelines Quantitative Finance & Algorithmic Trading Quantitative Analysis & Quantitative Research Algorithmic Trading & Trading Bots Trading Strategy Development & Automation Backtesting & Strategy Optimization Financial Modeling & Forecasting Portfolio Analytics & Risk Analysis Market Data Analysis Technical Indicators & Trading Signals Cryptocurrency & Financial Markets I've helped businesses and traders: 1. Build predictive models for customer churn, demand forecasting, sales forecasting, and market analysis. 2. Develop algorithmic trading strategies and automated trading bots using Python, machine learning, and technical indicators. 3. Build Power BI and Tableau dashboards for financial, sales, operational, and KPI reporting. 4. Clean, validate, and transform complex datasets into analysis-ready data and automated reporting workflows. 5. Use clustering, segmentation, forecasting, and statistical analysis to uncover patterns and support better decisions. What makes me different? I bridge the gap between technical models and real-world business outcomes. I don't just build a model or dashboard and hand it over. I focus on understanding why the analysis is needed, what decision it should support, and how the solution can actually be used. Business-focused: I connect data and technical analysis to real business decisions. End-to-end: From SQL/Python data extraction and cleaning to analysis, modeling, visualization, and automation. Quantitative: Strong background in quantitative analysis, financial modeling, and algorithmic trading. Practical: I build solutions designed for real workflows, not just prototypes. Clear communication: I turn complex technical findings into insights that non-technical stakeholders can understand. Whether you need a Data Scientist, Data Analyst, Machine Learning Engineer, Quantitative Analyst, Quantitative Finance Specialist, or Algorithmic Trading Developer, I can help. Send me your project requirements or data problem, and let's discuss the best way to solve it.