About – Samuel Nnitiwe Theophilus

Samuel Nnitiwe Theophilus is an AI engineer and consultant who helps businesses turn AI capabilities into reliable, production-ready systems. His work spans LLM applications, RAG and GraphRAG, AI evaluation, MLOps/GenAIOps, data engineering and workflow automation – from system design and integration to deployment, monitoring and optimization.

He also builds AI-powered products, including CV Booster AI, with hands-on experience taking AI from concept to real-world use. His focus is making AI dependable, cost-efficient and useful in the workflows where businesses need it most.

AI Engineering LLM Applications RAG / GraphRAG AI Agents MLOps / GenAIOps AI Evaluation Data Engineering Workflow Automation

The unglamorous parts are where trust is won.

Most AI projects do not fail at the model. They fail at retrieval that returns the wrong passage, evaluation that never happened, and pipelines that quietly get slow and expensive. I spend my time on exactly those parts: the retrieval that returns the right evidence, the eval that catches a wrong answer before a user does, and the pipeline that still runs cheaply at 3am.

That focus shows up in the numbers – a production GenAI pipeline run at 90% lower inference cost and 70% greater efficiency, duplicates cut from roughly 7,000 to 250 a day, and a 74% lift in retrieval relevance on an earlier RAG build.

Seven years across data, ML and production AI.

2026 – present · Data2Bots

AI Engineer

Design and productionize generative-AI pipelines on the cloud, with the MLOps – CI/CD, monitoring, evaluation – that keeps them reliable and affordable in production.

2025 – present · CareAid (CASI)

Information Management (Contract)

Built data-collection systems and executive dashboards supporting humanitarian response across many field sites, with careful handling of sensitive, high-volume data.

2022 – 2026 · Correlation One

Data Analytics & Data Engineering TA

Mentored and evaluated hundreds of learners in Python, SQL and analytics, contributing to strong completion and satisfaction scores.

2023 – 2026 · Independent / Upwork

AI Engineer & Technical Writer

Delivered RAG systems, AI APIs and automation for clients, and wrote widely-read tutorials on building practical AI.

2021 – 2022 · Omdena

Machine Learning Engineer

Collaborated with large distributed ML teams on applied projects, from agricultural computer vision to anomaly detection.

I teach what I build.

Alongside client work I publish hands-on tutorials and build-alongs on the Nnitiwe channel – practical walkthroughs of the same techniques behind the production systems.

Nnitiwe on YouTube

Build-alongs, tutorials and AI deep-dives

From RAG and agents to evaluation and deployment – watch how the pieces fit together, then try them yourself.

Watch on YouTube

Where I add the most value.

A good fit

Document-heavy or regulated workflows that need reliable extraction and retrieval; an AI prototype that has to become dependable in production; an existing AI system that is inconsistent, slow or expensive; or a repetitive, multi-step process worth automating with human oversight.

Probably not me

Pure AI video or image generation, generic marketing-content automation, or work where the goal is to adopt AI for its own sake rather than to solve a defined business problem. I will say so early, and point you somewhere better.

Building AI that businesses can actually rely on.

The unglamorous parts are where trust is won.

Seven years across data, ML and production AI.

AI Engineer

Information Management (Contract)

Data Analytics & Data Engineering TA

AI Engineer & Technical Writer

Machine Learning Engineer

I teach what I build.

Build-alongs, tutorials and AI deep-dives

Where I add the most value.

A good fit

Probably not me

Have a problem worth solving properly?