Isaac Oselukwue

Computer Vision · Machine Learning · Edge Deployment

Isaac Oselukwue

Computer Vision & Machine Learning Engineer

I build and deploy real-time perception systems — detection, segmentation, depth and pose — running on ROS2 and TensorRT at the edge. Six years of backend and payments engineering underneath it, so the models actually ship.

Lincoln, United Kingdom Available for high-impact roles

  • 30 FPSReal-time perception on Jetson
  • <15msEdge inference latency
  • 600+TPS payments delivered
  • 6 yrsShipping software
Portrait of Isaac Oselukwue

Profile

Lincoln, UK · 53.2307° N

I'm a Computer Vision and Machine Learning Engineer based in Lincoln. Right now I build real-time perception for agricultural robotics at Agaricus Robotics — detection, depth and pose estimation, optimised to hit latency budgets on edge compute.

Before robotics I spent six years engineering high-throughput backend and payment systems across fintech, which is where I learned to ship software that stays reliable under real load. I care about measurable outcomes, tight latency budgets and code that holds up in production rather than just in a notebook.

I work across robotics, computer vision and applied LLMs, with a background in event-driven systems, high-throughput payments and sustainability research.

Education

M.Sc. Computer Science University of Lincoln · 2024 — 2025 B.Sc. Computer Science University of Lagos · 2015 — 2021

Certifications

  • Microsoft Azure Fundamentals
  • Microsoft Azure AI Fundamentals
  • Microsoft Azure Data Fundamentals
  • Microsoft Power Platform Fundamentals
  • Security, Compliance & Identity Fundamentals
  • ITIL Foundation

Experience

06 roles · 2020 — present
  • Software Engineer — Agaricus Robotics

    Built and deployed a multimodal perception stack for real-time harvesting — YOLO-based detection, depth pose estimation and classical CV quality cues. Added telemetry for ML-driven decisions and optimised models with ONNX/TensorRT-style workflows to meet real-time constraints on edge compute. Watch the interview ↗

    Lincoln, UK Oct 2025 — Present
  • Full-Stack & Data Engineer — University of Lincoln

    Built Python dashboards visualising strawberry-field sensor data and image metrics for ML experiments, automated dataset preprocessing and migration pipelines, and developed geospatial features — habitat rasterisation and connectivity overlays — over an Elixir/Phoenix streaming backend.

    Lincoln, UK May 2025 — Sep 2025
  • Software Engineer — Zedvance Finance

    Delivered core Business Plus banking features — identity, POS transactions and account creation — for a 30% efficiency gain and 40% faster setup. Co-built a fine-grained permissions library and a Saga orchestration library that cut POS failure rates by 25%, backed by 200+ tests.

    Lagos, Nigeria Apr 2024 — Sep 2024
  • Application Developer — FCMB Group

    Architected asset-management services as independent, scalable microservices and moved communication to event-driven Kafka, reducing message failures by 32% and deployment time by 8%.

    Lagos, Nigeria Dec 2023 — Apr 2024
  • Software Engineer — Sterling Bank

    Redesigned Nigeria's NIP instant-payment services to handle 600+ transactions per second with auto-scaling, while cutting infrastructure cost by 78%. Built SOAP-to-REST middleware and optimised wallet services for faster response times.

    Lagos, Nigeria Oct 2021 — Dec 2023
  • Software Developer — CoralPay Technologies

    Built and maintained full-stack ASP.NET MVC payment integrations and designed a Biller Aggregator Platform connecting multiple payment providers.

    Lagos, Nigeria Aug 2020 — Sep 2021

Featured Interview

Agaricus Robotics · 04:03
Real-time perception work at Agaricus Robotics, explained — plays from 4:03.

Selected Projects

Vision · ML · Systems
  • VQA

    Fine-tuned ResNet, ViT, BLIP and Phi-4 with LoRA and selective layer freezing for multimodal visual question answering.

    PyTorch · LoRA · ViT · BLIP
  • Audio.AI

    Audio transcription and summarisation pipelines built on Whisper and Deepgram, with parallel implementations in .NET and Python.

    Whisper · Deepgram · .NET · Python
  • Media Locator

    Full-stack Blazor/C# application with end-to-end testing (NUnit, Moq, Testcontainers, Playwright), GitHub Actions CI/CD and Docker support.

    Blazor · C# · Playwright · Docker
  • ILS

    Open-source integrated library system built on a TCP client/server architecture in C++ with a domain-driven design.

    C++ · TCP · DDD
  • SPSS

    Responsive web application for the University of Lincoln Careers team, built with Blazor, Bootstrap and CSS.

    Blazor · Bootstrap · CSS

Stack

Current working set
Languages
Python C++ C# JavaScript
Computer Vision
YOLO RF-DETR SAM ResNet ConvNeXt Depth Anything v2 DPT ViT BLIP OpenCV
Vision tasks
Detection Segmentation Classification Depth estimation Pose estimation Tracking VQA
ML & MLOps
PyTorch Transformers LoRA ONNX MLflow Whisper Ollama
Robotics & Edge
ROS2 TensorRT NVIDIA Jetson Edge inference Docker
Backend & Data
FastAPI .NET Kafka Redis PostgreSQL MSSQL MongoDB
Tooling
Git CI/CD TDD Microservices Azure

Open Source

github.com/isaacoselukwue
  • yolo26-eval

    Production-grade YOLO26 object detection: exported to ONNX and served with FastAPI + ONNX Runtime — no PyTorch at runtime, cutting the container from ~2.2 GB to ~350 MB and cold-start under 2 s. Single-call inference via YOLO26's NMS-free head.

    Python · ONNX Runtime · FastAPI · Docker
  • xdu

    Cross-platform clone of the POSIX du disk-usage tool in portable C++17 — real allocated-block accounting with byte-for-byte GNU du parity, hard-link/cycle de-duplication, JSON/CSV output and a differential test suite.

    C++17 · CLI · Cross-platform
  • LibraryManager

    C++ project demonstrating architecture discipline and domain-driven design.

    C++ · DDD
  • NIPEncryptionUtility

    Security-focused C# utility for encryption workflows.

    C# · Security

Contact

Open to work

Engineering partner for teams that need real-time computer vision, reliable systems and practical edge ML — shipped, measured and maintained, without the hype.