You will get AI-driven Route Optimization | AI for Logistics | Mathematical optimization
Top Rated
Project details
I offer cutting-edge AI-driven route optimization solutions tailored for logistics and supply chain efficiency. By combining advanced mathematical optimization methods and machine learning technologies like Python, TensorFlow, and PyTorch, I design systems that minimize costs, save time, and enhance resource allocation.
Using predictive modeling and reinforcement learning, I create intelligent algorithms to streamline route planning for fleets, deliveries, and supply chains. With tools like NumPy, pandas, and SciPy, I develop data-driven models that adapt dynamically to changing conditions. Whether it’s handling time-sensitive deliveries or complex multi-drop routes, my solutions ensure precision and scalability.
By integrating cloud-based platforms like Amazon SageMaker, Google AutoML, and Azure Machine Learning, I build scalable AI systems ready for real-world applications. Leveraging technologies such as YOLO for vehicle tracking and OpenCV for spatial data analysis, I provide tools to enhance route visualization and monitoring.
Let’s transform your logistics operations with AI-powered tools for smarter, faster, and more efficient decision-making.
Using predictive modeling and reinforcement learning, I create intelligent algorithms to streamline route planning for fleets, deliveries, and supply chains. With tools like NumPy, pandas, and SciPy, I develop data-driven models that adapt dynamically to changing conditions. Whether it’s handling time-sensitive deliveries or complex multi-drop routes, my solutions ensure precision and scalability.
By integrating cloud-based platforms like Amazon SageMaker, Google AutoML, and Azure Machine Learning, I build scalable AI systems ready for real-world applications. Leveraging technologies such as YOLO for vehicle tracking and OpenCV for spatial data analysis, I provide tools to enhance route visualization and monitoring.
Let’s transform your logistics operations with AI-powered tools for smarter, faster, and more efficient decision-making.
Machine Learning Tools
BERT, ChatGPT, deeplearn.js, Deeplearning4j, GitHub Copilot, Google AutoML, GPT-3, Keras, Kubeflow, Mapr, MATLAB, MLflow, NLTK, NumPy, Open Neural Network Exchange, OpenCV, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, R, SciPy, Tableau, TensorFlowWhat's included $16,000
These options are included with the project scope.
$16,000
- Delivery Time 20 days
- Number of Revisions 1
- Number of Model Variations 2
- Number of Scenarios 10
- Number of Graphs/Charts 5
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
- Source Code
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About Pavlo
AI Engineer | Data Scientist | Machine Learning | Computer Vision
100%
Job Success
Kharkiv, Ukraine - 7:50 am local time
I'm an AI Engineer building production-grade AI agent and RAG systems, not simple prompt wrappers. With 8+ years in software engineering and AI development and $500K+ earned on Upwork, I hold Top Rated Plus status and our team is ranked among the Top 10 Machine Learning agencies on the platform. I work with companies that need an AI system to actually run in production, handle real user traffic, and stay accurate, not a demo that breaks on the first edge case.
As an AI Engineer, my core work covers retrieval-augmented generation pipelines, agentic workflows with tool calling and structured outputs, prompt engineering, and LLM integration with OpenAI, Anthropic, and Gemini APIs. This is the kind of system clients need an AI Engineer for: not a chatbot that answers FAQs, but an agent that retrieves the right information, calls the right tools, and follows through on what the user actually needs, with hallucination reduction and evaluation built in from the start.
As a Machine Learning Engineer and Data Scientist, I build systems for structured and time-series data: demand forecasting, anomaly detection, biomedical signal analysis, and structural health monitoring. My data scientist workflow covers Python, scikit-learn, pandas, NumPy, and SciPy alongside deep learning frameworks including TensorFlow, PyTorch, and Keras, with experiment tracking and evaluation metrics to ensure models perform consistently in production. When a project needs predictive or classification models alongside the AI agent itself, I own that layer too as a Machine Learning Engineer.
As a Computer Vision Engineer and software engineer, I build object detection, multi-object tracking, pose estimation, and image segmentation systems using OpenCV, YOLO, and deep learning architectures. Where this intersects with the AI Engineer work is in multimodal systems and computer vision agents: I work with Vision Language Models (VLMs) to build AI pipelines that understand images and video, not just text. Most AI Engineers only work with text. I bring production computer vision and deep learning experience on top of the LLM layer, which matters for any product where the AI needs to see, not just read.
On the engineering side, I work as a Python developer and software engineer building backend services with FastAPI, vector databases including Pinecone and pgvector, and LangChain or custom orchestration for multi-step agent logic. When a project needs full ownership of both the AI layer and the surrounding application, I work as a Full Stack AI Developer, handling backend APIs, database design, and frontend integration so the AI system ships as a complete product, not just a backend script.
I work with a specialized team that includes a computer vision PhD, deep learning researchers, and mathematical optimization specialists. This lets me scope larger systems, split orchestration, retrieval, and evaluation work across the team, and deliver a full AI Engineer and Machine Learning Engineer engagement faster than a solo contributor could, with the software engineering discipline of clean APIs, logging, and testing baked in from day one.
Clients typically work with me when they need:
- an AI Engineer to build a RAG pipeline, AI agent, or chatbot that actually works in production
- a Machine Learning Engineer or Data Scientist to build predictive models or structured data pipelines
- a Computer Vision Engineer to add visual understanding or VLM-based reasoning to an AI product
- a Software Engineer who understands LLM orchestration, tool calling, vector search, and backend architecture
- a Python developer who can own the full stack from model training to deployed API
If you need an AI Engineer and a software engineer with the full stack from prompt design to production deployment, let's talk.
Main stack: Python, OpenAI API, Anthropic Claude, Gemini, LangChain, LangGraph, LlamaIndex, FastAPI, Pinecone, pgvector, TensorFlow, PyTorch, Keras, OpenCV, YOLO, Docker, PostgreSQL, JavaScript, Git.
Steps for completing your project
After purchasing the project, send requirements so Pavlo can start the project.
Delivery time starts when Pavlo receives requirements from you.
Pavlo works on your project following the steps below.
Revisions may occur after the delivery date.
𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲
I start with an in-depth discovery phase to understand your target audience, market trends, and the unique needs of your AI-Powered web application.
𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐐𝐀
During the development phase, I focus on building your AI-driven web app, adhering to the project requirements and scope. My process includes thorough testing to ensure a seamless and responsive user experience across various devices and browsers.