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Michael Adetoro

@michaeladetoro

AI Engineer delivering production-ready computer vision and LLM solutions.

Nigeria
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What I'm looking for

I seek roles where I can build and deploy production AI systems, lead model and MLOps efforts, work in cross-functional teams, and deliver measurable impact in product-focused environments.

I am an AI Engineer with 4+ years building end-to-end AI solutions across HealthTech, Supply Chain, Logistics, RecTech, Banking, and Energy. I focus on turning data into actionable systems that improve decision-making and operational outcomes.

I have designed and deployed production systems including conversational multi-agent health chatbots, computer vision diagnostic modules, RAG-enabled support bots, and candidate-matching engines. I lead model development, fine-tuning, prompt engineering, and CI/CD for continuous monitoring and performance improvements.

My technical toolkit includes Python, TensorFlow, PyTorch, LangChain, LangGraph, OpenAI/Hugging Face models, cloud platforms (AWS, Azure), containerization with Docker, and databases such as Postgres, MongoDB, and MySQL. I also apply MLOps practices using MLflow, DVC, and automated pipelines to ensure reproducibility and reliability.

I am passionate about building scalable AI features that deliver measurable impact—reducing manual processing, accelerating response times, and improving matching accuracy—while mentoring teams and driving product-focused engineering efforts.

Experience

Work history, roles, and key accomplishments

Education

Degrees, certifications, and relevant coursework

Federal University of Technology, Akure logoFA

Federal University of Technology, Akure

Bachelor of Technology, Technology / Engineering

2018 - 2024

Grade: 4.65/5.00 (First Class Honors)

Activities and societies: President, Information Systems Students’ Association (FUTA Chapter); Microsoft Learn Student Ambassador; participated in multiple machine learning and AI project competitions.

Completed a B.Tech. degree with First Class Honors focusing on technology and applied engineering concepts, graduating with a CGPA of 4.65/5.00.

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