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Oluwaseun Adeyo

@oluwaseunadeyo

Message

I build production LLM, RAG, and MLOps systems across AWS and Azure.

United Kingdom

Message

What I'm looking for

I'm open to remote ML/AI engineering and cloud architecture roles, and relocation for the right team. I want to build production LLM systems with teams that value reliability, security, cost efficiency, and accuracy.

At CloudPlexo, I design and deploy full-stack ML pipelines with SageMaker, Lambda, Bedrock, and S3, taking systems from prototype to production. I fine-tune GPT and BERT models and build RAG agents that reduce support response times by 35% and increase user satisfaction by 25%.

Previously at SEAMFIX, I built LangChain and OpenAI chatbots, automated ML pipelines, data platforms, and REST APIs. My work cut customer response times by 45%, improved data access by 60%, and enabled model releases in under two hours.

Across fintech, healthtech, and enterprise work, I focus on reliable, secure, cost-efficient AI systems using AWS, Azure, Python, PyTorch, Docker, Kubernetes, and Terraform.

Experience

Work history, roles, and key accomplishments

Current

Senior AI Cloud Architect

Current

CloudPlexo

Apr 2025 - Present (1 year 4 months)

- Designed and deployed full-stack ML pipelines using AWS services like SageMaker, Lambda, Bedrock, and S3 — from initial prototype to production-ready deployment.
- Developed and fine-tuned LLMs (GPT, BERT) for customer-facing applications, achieving 20% boost in contextual accuracy and 30% faster response times.
- Implemented RAG pipelines for intelligent agents, reducing average support respons

Deep Learning MLOps Cloud Computing Python AWS Sagemaker Lambda Bedrock S3 GitHub Actions AWS CodePipeline MLFlow EventBridge Amazon Transcribe

Senior AI / ML Engineer

SEAMFIX

Oct 2022 - Apr 2025 (2 years 6 months)

- Developed a LangChain and OpenAI chatbot, cutting customer response time by 45% and increasing satisfaction by 20%.
- Centralized multi-source data into Redshift with Lambda and AWS DMS, improving data access by 60% and halving report turnaround time.
- Developed and maintained end-to-end automated pipelines that run, monitor, and retrain ML models using best DevOps / MLOps tools and practices l

Deep Learning MLOps Cloud Computing

Data Scientist | ML Engineer

SEAMFIX

Nov 2018 - Oct 2022 (3 years 11 months)

- Applying engineering best practices to data science: CI/CD, containerization, unit testing, peer review.
- Deployed and monitored 2+ ML classifiers in production, adding automated drift monitoring that cut silent model failures and kept accuracy above target across releases
- Built and optimized ML models (TensorFlow, PyTorch, ONNX) for on-device and cloud deployment, including a facial-matching

Deep Learning MLOps Cloud Computing

Network Software Developer

CITS

Jan 2017 - Jul 2017 (6 months)

Worked as Network Software Developer at CITS.

Education

Degrees, certifications, and relevant coursework

Udacity

Cloud DevOps Engineering with AWS Nanodegree Program

2022 - 2022

Cloud Fundamentals
- Project: Deploy Static Website on AWS
- Services: S3, CloudFront, IAM

Deploy Infrastructure as Code (IAC)
- Project: Deploy a high-availability web app using CloudFormation
- Services: EC2, VPC, IAM, CloudFormation, RDS, S3

Build CI/CD Pipelines, Monitoring & Logging
- Project: Give Your Application Auto-Deploy Superpowers (Ansible and Circle CI)
- Services: EC2, S3, CloudF