You will get AI supply chain system with food waste reduction and demand forecasting
Project details
ou will get a production-ready AI supply chain platform that reduces food waste, predicts demand, and optimises distribution routes — built on three ML models working together in a single system.
The demand forecasting engine uses a Temporal Fusion Transformer with attention mechanisms to predict 1–30 days ahead with uncertainty quantification. The computer vision module uses EfficientNet-B4 to grade produce quality in under 50ms across five classes. The route optimisation layer uses a Graph Neural Network to minimise transport cost and cold chain risk simultaneously.
The platform is backed by 787,000+ food items from the USDA FoodData Central dataset, includes a real-time Dash dashboard, a FastAPI REST API with OAuth2 authentication, Redis caching, SQL Server integration, and a full monitoring stack with Prometheus and Grafana.
I am an AI and ML engineer based in Oslo, Norway, with two years of production experience delivering data platforms and ML systems at Norwegian companies.
You receive full source code, setup instructions, a working API, and a system deployable with Docker or Kubernetes.
The demand forecasting engine uses a Temporal Fusion Transformer with attention mechanisms to predict 1–30 days ahead with uncertainty quantification. The computer vision module uses EfficientNet-B4 to grade produce quality in under 50ms across five classes. The route optimisation layer uses a Graph Neural Network to minimise transport cost and cold chain risk simultaneously.
The platform is backed by 787,000+ food items from the USDA FoodData Central dataset, includes a real-time Dash dashboard, a FastAPI REST API with OAuth2 authentication, Redis caching, SQL Server integration, and a full monitoring stack with Prometheus and Grafana.
I am an AI and ML engineer based in Oslo, Norway, with two years of production experience delivering data platforms and ML systems at Norwegian companies.
You receive full source code, setup instructions, a working API, and a system deployable with Docker or Kubernetes.
Machine Learning Tools
Azure Machine Learning, Keras, MLflow, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$175
|
Standard
$299
|
Advanced
$575
|
|---|---|---|---|
| Delivery Time | 5 days | 9 days | 12 days |
Number of Revisions | 2 | 4 | 6 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 2 | 5 | 8 |
Number of Graphs/Charts | 3 | 7 | 12 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Computer vision quality grading module
(+ 3 Days)
+$75
IoT cold chain monitoring integratio
(+ 2 Days)
+$60
GNN route optimization engine
(+ 3 Days)
+$80Frequently asked questions
About Md Saidul
AI and LLM Engineer - RAG, LLM, Fraud Detection
Oslo, Norway - 11:47 pm local time
For the past two years I've been working professionally in Norway, first at Rolog Solutions as an R&D engineer building an AI-powered monitoring system for 50+ industrial robots, then at Jobswoop as a software engineer running a production data platform on Azure with 99.5% uptime over ten months. Before that, I completed a Master's in Computer Science at UiT — The Arctic University of Norway, where most of my thesis work ended up running in production rather than sitting in a paper.
The kind of work I do: production LLM systems, RAG pipelines, fraud and anomaly detection, agentic AI with tool-calling, EU-compliant cloud infrastructure. Not prototypes — systems that handle real data, real users, and real consequences if something breaks.
Some concrete results: a fraud detection ensemble that reached AUC 0.9980 with Precision 1.0 and Recall 0.96. An agentic RAG platform running on Azure Norway East with GPT-4o tool-calling and real-time streaming, built under Norwegian GDPR. An anomaly detection system that cut false positives by 40% across two million records. A 60% reduction in robot troubleshooting time for a Norwegian robotics company. A 35% reduction in supply chain waste through predictive analytics.
I work across the full stack — LangChain, OpenAI, FastAPI, PostgreSQL, Azure, Terraform, Docker, Kubernetes. I write the models, the APIs, and the infrastructure. I've built to GDPR, EU AI Act, and HIPAA on live systems, so compliance is not something I need to learn on your project.
I'm based in Oslo, which means full working-day overlap with European clients and a reasonable window with the US East Coast. For clients in Asia or the Pacific I'm comfortable working async — I document clearly, communicate proactively, and don't go quiet between updates.
My Upwork track record is new. My engineering track record is not. If the project looks right, I'd genuinely like the chance to prove it.
Steps for completing your project
After purchasing the project, send requirements so Md Saidul can start the project.
Delivery time starts when Md Saidul receives requirements from you.
Md Saidul works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and data review
I review your supply chain structure, data format, and key metrics. I deliver a short technical spec for sign-off before any code is written.
Data pipeline and feature engineering
I build the ingestion pipeline, validation layer, and feature engineering modules adapted to your product and time series schema.