Industrial IoT · Data Systems · Industry 4.0

Emmanuel
Akingbade

EE Graduate & Data Systems Engineer

I build data systems across the IoT and IIoT ecosystem, from edge devices and field protocols to cloud platforms and AI pipelines. My background in electrical and electronics engineering means I work comfortably across the full stack, from what happens on the wire to what happens with the data at scale.

View Projects GitHub ↗ LinkedIn ↗
Emmanuel Akingbade
EA
Open to opportunities
Background

Where Field Systems
Meet Intelligent Data

I graduated with a B.Eng in Electrical and Electronics Engineering from Redeemer's University and currently work as a Graduate Engineer in Industrial IoT and Data Systems at Cors System, Lagos.

My focus is the intersection of physical industrial systems and the data infrastructure needed to observe, model, and optimise them. That means working across the full IoT and IIoT stack: field-level protocol integration, edge AI inference, real-time streaming, and cloud data engineering on platforms like Azure and Databricks.

Most data engineers don't understand what's happening on the wire. Most EE graduates don't build streaming pipelines or train vision models. I'm building toward both, with a clear focus on Industry 4.0, industrial automation, and digital twin infrastructure.

All the projects here are original, self-directed work.Here, I build things I want to exist.

Industrial Protocols
Modbus TCP/RTU OPC UA MQTT XBee LoRa
Streaming
Redpanda Kafka
Data Engineering
Python PostgreSQL Airflow dbt MinIO Azure Databricks
Observability
Prometheus Grafana
Vision & ML
YOLO OpenCV PyTorch
Infrastructure
Docker Docker Compose FastAPI React
Projects

Building Toward
Industry 4.0

01
StreamForge
Protocol-agnostic industrial data gateway, OT to IT, zero data loss

A Redpanda-backed streaming platform that bridges Operational Technology and Information Technology in industrial environments. Supports Modbus TCP/RTU, OPC UA, MQTT, XBee, and LoRa natively. Built around a clean separation between the control plane (configuration, orchestration, UI) and the data plane (edge daemon, protocol adapters, sink services). Designed for edge-first, offline-capable, hybrid-cloud deployment across manufacturing, oil and gas, and utilities.

Redpanda OPC UA Modbus MQTT FastAPI React Docker Edge Computing Python
GitHub ↗
02
CVops
Real-time computer vision pipeline, edge AI inference for industrial safety

A distributed vision pipeline built for edge-to-cloud video analytics. IoT cameras feed into a streaming microservice chain: preprocessing, YOLO11 object detection, MinIO object storage. Full Prometheus and Grafana observability baked in from the start, covering FPS, latency, detection confidence, consumer lag, and service health in real time. Currently being refactored toward offshore safety monitoring and industrial defect detection use cases.

YOLO11 Edge AI Industrial Safety Kafka OpenCV PyTorch Prometheus Grafana MinIO Docker
GitHub ↗
03
DigitalThread
Process simulation and real-time state mirroring for industrial assets

In design. Will integrate with StreamForge as the live data source layer and extend into real-time process simulation, state estimation, and anomaly detection for industrial equipment. Target use cases include process plant simulation and predictive maintenance scenarios.

Digital Twins Process Simulation State Estimation Industry 4.0 Python
⬡ IN DESIGN
Contact

Let's Work
On Hard Problems

I'm open to roles, collaborations, and conversations in industrial automation, IIoT, robotics, and digital twins. If you're building something at the intersection of physical systems and data infrastructure, I'd like to hear about it.

LinkedIn Emmanuel Akingbade
GitHub Akin-ctrl
Gmail akin4gbade@gmail.com
Open to opportunities in
  • IIoT gateway and edge computing systems
  • Edge AI and industrial computer vision
  • OT/IT integration and industrial data platforms
  • Cloud data engineering (Azure, Databricks)
  • Digital twin architecture and process simulation
  • Industrial robotics
  • Data infrastructure