You will get Computer Vision Engineer for CV Pipeline Audit & Roadmap
Top Rated
Project details
As an experienced Computer Vision Engineer, I'll review your existing computer vision pipeline and deliver a clear roadmap for improving accuracy, performance, and production readiness. This audit covers your current OpenCV or deep learning model architecture, training data quality, inference speed, and deployment setup.
Working as a Computer Vision Engineer and machine learning specialist, I identify the root causes behind accuracy issues, latency bottlenecks, or scaling problems, and document specific, actionable fixes. This is the right starting point if you have an existing detection, tracking, or segmentation system that isn't performing as expected, or if you're deciding whether to build in-house or bring in outside Computer Vision Engineer expertise.
Deliverable is a written technical report plus a follow-up call to walk through findings and prioritize next steps.
Working as a Computer Vision Engineer and machine learning specialist, I identify the root causes behind accuracy issues, latency bottlenecks, or scaling problems, and document specific, actionable fixes. This is the right starting point if you have an existing detection, tracking, or segmentation system that isn't performing as expected, or if you're deciding whether to build in-house or bring in outside Computer Vision Engineer expertise.
Deliverable is a written technical report plus a follow-up call to walk through findings and prioritize next steps.
Machine Learning Tools
Amazon SageMaker, AnyLogic, ChatGPT, GitHub Copilot, Google Sheets, Keras, Microsoft Excel, NumPy, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, R, scikit-learn, SciPy, SQL, Tableau, TensorFlow, Tesseract OCR, Vertex AI, XGBoostWhat's included
| Service Tiers |
Starter
$800
|
Standard
$1,800
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 18 days |
Number of Revisions | 1 | 2 | 2 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - |
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We have nothing but positive things to say about Vadym and his team. They delivered a project on time and were able to fix a few things that we had gotten wrong. Will be happy to work with them again on future projects!
MS
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Feb 12, 2024
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We enjoyed working with Vadym. He is very good in what he does and he helped us a lot.
We will recommend him to any one requiring a Python developer or the likes.
Thanks
We will recommend him to any one requiring a Python developer or the likes.
Thanks
JF
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Nov 30, 2023
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Vadym's expertise in AI development was instrumental to our project's success. His innovative solutions, coupled with efficient coding practices, significantly enhanced our project's capabilities. His ability to communicate complex technical concepts clearly was invaluable. I highly recommend Vadym for his exceptional skills and dedication.
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Mridula S.
Oct 3, 2022
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About Vadym
Computer Vision Engineer | Data Scientist | Machine Learning | AI
100%
Job Success
Kharkiv, Ukraine - 2:17 am local time
I'm a Computer Vision Engineer and Machine Learning Engineer with 7+ years delivering production-grade AI systems. Upwork has Expert-Vetted me as a Top 1% specialist in this niche, and our team is ranked among the Top 10 Machine Learning agencies on Upwork. I work with product teams and startups across sports analytics, industrial inspection, satellite and aerial imagery, access control, healthcare, and generative AI — any domain where visual data needs to become reliable, actionable output running in production.
As a Computer Vision Engineer, my core work covers object detection, multi-object tracking, pose estimation, image segmentation, image processing, and real-time video analysis. I build end-to-end pipelines in Python using OpenCV, PyTorch, TensorFlow, and Keras, from dataset preparation and model training through TensorRT optimization and Docker deployment on cloud or NVIDIA Jetson edge hardware. I use C++ for performance-critical components where Python latency is a bottleneck.
The domains where computer vision engineer experience creates the most value: sports analytics (player tracking, performance metrics, automated statistics from broadcast video), industrial inspection (defect detection and quality control on production lines), satellite and aerial imagery (object detection and segmentation for infrastructure analysis), access control and security (vehicle identification, multi-camera real-time monitoring), and healthcare and biomechanics (pose analysis, body measurement, and biomedical signal processing connected to AI coaching backends).
As a Machine Learning Engineer and Data Scientist, I also build systems for structured and time-series data: demand forecasting, anomaly detection, biomedical signal analysis, and structural health monitoring. My data scientist workflow covers scikit-learn, pandas, NumPy, and SciPy alongside deep learning frameworks, with experiment tracking and evaluation metrics to ensure models perform consistently in production. When projects require generative AI or LLM components, I deliver RAG pipelines with LangChain and vector databases, synthetic dataset generation tools, and document processing systems using the Gemini API.
Regardless of domain, the computer vision engineer approach stays the same: combine OpenCV-based preprocessing with deep learning inference into a scalable, testable pipeline that holds up under real-world conditions — variable lighting, occlusion, low resolution, multi-camera setups, and edge hardware constraints. I work with YOLO-family models, ByteTrack and DeepSORT for tracking, MediaPipe and MMPose for pose estimation, TensorRT and ONNX for inference optimization, and FastAPI with Docker for production deployment.
I work with a specialized team that includes a computer vision PhD, deep learning researchers, and mathematical optimization specialists. This lets me scope complex systems, split parallel workstreams, and deliver a full Computer Vision Engineer engagement faster than a solo contributor could.
Clients typically work with me when they need:
- a Computer Vision Engineer to build detection, tracking, or segmentation systems from scratch
- a Machine Learning Engineer to productionize a research model and meet latency requirements
- a Data Scientist who can go from raw data to a deployable model end-to-end
- an AI engineer to integrate LLM or generative components into an existing backend
- real-time or edge inference optimized for NVIDIA Jetson or mobile deployment
- a Python developer who understands both the AI pipeline and the surrounding system architecture
If you need a Computer Vision Engineer with the full stack from dataset to deployed API, let's talk.
Main stack: Python, OpenCV, PyTorch, TensorFlow, Keras, YOLO, ByteTrack, MediaPipe, MMPose, CoreML, TFLite, TensorRT, ONNX, scikit-learn, FastAPI, Docker, PostgreSQL, C++, NumPy, pandas, SciPy.
Steps for completing your project
After purchasing the project, send requirements so Vadym can start the project.
Delivery time starts when Vadym receives requirements from you.
Vadym works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery
Review your current computer vision pipeline, codebase, and performance data to understand where it's falling short.
Development and QA
Analyze model architecture, data quality, and inference setup; identify root causes and validate findings against your video data.