Cloud & DevOps engineer who also builds the AI systems that run on that infrastructure. Four years turning Terraform, Kubernetes, and CI/CD into production platforms — now doing the same for LLM agents and ML pipelines.
I started in cloud and DevOps — designing AWS infrastructure with Terraform, running Kubernetes clusters, and building the CI/CD pipelines that let teams ship without fear. Across fintech, payments, and edtech platforms, that meant improving deployment reliability, cutting operational overhead, and keeping production systems observable and stable.
More recently I've moved into AI engineering — building LLM-powered agents, automation pipelines, and ML systems that need the same rigor: reproducible deployments, monitoring, and infrastructure that scales. I like sitting at the point where the two meet, because that's usually where AI projects actually make it to production.
Based in Lagos, Nigeria. Open to remote and hybrid roles across cloud engineering, MLOps, and AI infrastructure.
Three distinct, deliberately-chosen capabilities — retrieval, multi-agent orchestration, and enterprise safety — each proven with a production-grade personal build.
Production-grade hybrid retrieval combining dense semantic search (ChromaDB) with sparse keyword retrieval (BM25), unified through cross-encoder reranking — fully containerized with Docker.
A production-grade multi-agent system built with CrewAI and LangChain, featuring Human-In-The-Loop interception and automated web research compilation — fully containerized with Docker.
A secure LLM proxy gateway and observability dashboard with automated PII sanitization, prompt-injection guardrails, cost logging, and real-time Streamlit visualization.
Beyond the three pillars above: production cloud infrastructure and additional AI/ML systems from four years across fintech, payments, and AI-native teams.
Designed and provisioned AWS infrastructure across multiple environments with Terraform for a live payment and transaction platform, with Python automation for validation and deployment workflows.
Built and optimized ML-driven relevance scoring for a job-matching platform, alongside AI automation pipelines for the surrounding deployment workflow.
Engineered secure, scalable AWS infrastructure for a fintech platform using reusable Terraform modules, with CI/CD pipelines and VPN-based secure connectivity for enterprise integrations.
Built agents capable of multi-step reasoning and task execution, then optimized the underlying models and workflows for reliability and lower manual intervention.
Standardized deployments with Terraform-based IaC and managed containerized workloads on Kubernetes and Docker, with GitHub Actions handling the automation layer.
Designed scalable AI pipelines for real-time interactions, working cross-functionally to move solutions from research into production.
Designed scalable AWS environments with load balancing and autoscaling, automating provisioning with Terraform and Ansible and CI/CD with GitLab and Jenkins.
A Python platform for automated log analysis and failure detection, with intelligent root-cause workflows to support operations teams — containerized and deployed via CI/CD.
Built CI/CD pipelines specifically for AI model deployment and implemented monitoring for AI services running on AWS, tuning infrastructure for cost and uptime.
Trained and evaluated large language models against best-practice benchmarks, reviewing and optimizing AI-generated code to improve model reliability.
A single, cohesive pipeline unifying data handling, safety auditing, MLflow experiment tracking, automated evaluation gating, and API preparation.
An agentic backend that turns raw video/audio into publish-ready show notes — transcription, visual analysis, and AI-generated metadata, with automatic publishing to Notion and YouTube.