Hire the Best Algorithms Engineers

I’m a software engineer with a strong focus on problem solving and algorithms. I’ve solved 7000+ algorithmic problems across platforms like LeetCode and Codeforces, ranking in the top 0.16% on LeetCode and achieving Candidate Master on Codeforces. My background is deeply rooted in data structures, algorithmic thinking, and writing efficient, scalable solutions under constraints. Beyond solving problems myself, I mentor students and engineers to master problem-solving skills for coding interviews and technical assessments. I focus on building strong fundamentals and recognizing patterns.

- CTO at Singularis Programming - Extensive experience in Computer Vision and Machine Learning projects. - Keen on cutting-edge technologies and helping businesses achieve their objectives. 🏅About company - 13+ years of continuous experience in programming - Our engineers got significant achievements and results in the Programming World Cup ACM ICPC - 200+ successfully completed projects 🤝 Trusted by Intel, NEC, Samsung, DHL, VIATechnik, Amberg Technologies - Working with partners around the world, in all time zones: in Europe, North America, MENA, the Asia-Pacific region. - Completed projects in the UK, Switzerland, the USA, Spain, Germany, Belgium and other of EU. 💡 OUR SERVICES include, but are not limited to: – Definition and Planning, Business Analysis, Project Planning – Back-end development + DevOps Application Architecture, REST API, Application development, CI/CD, .NET Stack, C#, Entity Framework, NHibernate, Microservices – Front-end development Mark-upping, Client-side development, Responsiveness, React, Typescript, JavaScript, Redux, MobX – AI / Machine Learning / LLM Deep Learning and convolutional networks, GAN, TensorFlow, Keras, Torch, Stable Diffusion, Transformer architectures, fine-tuning and optimization, Hugging Face, LangChain, RAG, prompt engineering, Unsloth, llama.cpp, vLLM, model quantization and deployment – Computer Vision OpenCV, C++/Python, MMCV, YOLO, dlib, developing applications for Kinect and Intel RealSense, Microsoft HoloLens, video processing with FFmpeg – Mobile development iOS, Android, Cross-platform – Testing/Quality Assurance Manual (TestLink), Unit Tests, Automation, Stress, checklists, test-cases, Cypress – Technical Support and Maintenance DB Back-ups, Cloud/Data migration, Bug Fixing, 24/7 support 💼 Main principles - Maximal quality of the provided services and the developed software. - Focus on permanent improvement of our team. - Stay tuned at the front edge of modern computer technologies - Apply the most advanced approaches to software development. 👋🏼 I’m open to collaborate. Feel free to get in touch with me and my team to discuss any project implementation — we’ll be happy to share valuable ideas and suggestions!

I help research teams and technical founders build reliable, testable scientific and quantitative software — from research prototypes and backtesting frameworks to production-grade execution systems, simulation engines, and performance optimization. I also conduct independent technical review of quantitative models, frameworks, and research. If you're dealing with slow code, messy data, a model you can't trust, or a research prototype that needs to become production, I can turn it into a robust, reproducible system. Credibility: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase (derivative pricing, 1M+ LOC C++ library). Max Planck Institute postdoc (general relativistic hydrodynamics simulations on HPC clusters, 1000+ cores). 13 peer-reviewed publications, 1,300+ citations, h-index 12. WHAT I DELIVER ▸ Quant & Options Engineering Backtesting frameworks (event-driven or vectorized), walk-forward, leakage checks Options analytics: Greeks, IV surfaces, Black-Scholes / numerical methods Research → production pipelines (clean architecture, tests, logging, monitoring) Execution integrations (e.g., IBKR) and robust order/risk handling Market data ingestion, cleaning, corporate actions handling, quality control checks ML and statistical models for time-series signals (proper time-series CV, no leakage) ▸ Scientific Computing & Research Tooling Custom software and research tooling for research-driven teams across physics, chemistry, biology, engineering and beyond — where the underlying problem is mathematical or computational. - Custom numerical solvers (finite volume / finite difference, spectral methods, particle methods), with stability and convergence analysis - Optimization engines (Bayesian optimization, gradient-based, evolutionary) for experimental design, formulation, and parameter search - Simulation frameworks for physical, chemical, and biological systems — from prototype to production-grade - Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows - Verification and validation: benchmarks, unit tests, regression tests, convergence studies - Analysis tools, dashboards, and reporting infrastructure for research workflows - Air-gapped and reproducible deployments where IP sensitivity or regulatory context requires it ▸ Quantitative & Mathematical Review (NDA-protected) Independent technical review of quantitative models, frameworks, and research Verification of internal consistency, identifiability, hidden assumptions, and mathematical correctness Assessment of whether the formal structure supports the conclusions drawn from it Review of implementation against specification: numerical stability, edge cases, and code-to-specification fidelity ▸ HPC & Performance Engineering Performance engineering for large-scale scientific and ML workloads Distributed computing (MPI/OpenMP/CUDA), GPU optimization, memory and I/O tuning, parallel architectures, inference and training infrastructure at scale. Profiling, refactors, and speedups for codebases that need to run reliably under production load. WHY CLIENTS WORK WITH ME - Trustworthy engineering: I turn research prototypes into tested, reproducible production code - De-risking: I find failure modes early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability: clean architecture, docs, and handover-ready delivery your team can extend - Communication: clear milestones, concise updates, realistic timelines — no surprises - Math ↔ engineering bridge: strong intuition for both theory and implementation details IDEAL PROJECTS - Quant strategy development, backtesting and research infrastructure - Options analytics and derivatives tooling - Mathematical review of quantitative manuscripts, white papers, or research frameworks - Independent validation of production models or implementations against specification - Market data pipeline and reproducibility upgrades - Performance optimization of slow Python/C++ codebases - Distributed training, GPU optimization, and inference serving for AI/ML workloads - Custom scientific or industrialized simulation or numerical software - Custom internal R&D tools for research labs and technical teams If this sounds like a fit, message me with a detailed brief with your current stack and success criteria — I'll let you know how I can help.

👋 Hi, I’m Mohamed — a Top Rated🏆 Senior Software Engineer. 🚀 I help companies design, build, and deliver high-performance software systems — from computer vision and AI pipelines to real-time desktop applications and embedded platforms. 🧩 What I deliver: ✅ Industrial Computer Vision Systems 🔹Integration with industrial cameras (Basler, Luxonis, FLIR / Point Grey) 🔹High-accuracy inspection, measurement, and defect detection 🔹Optimized OpenCV pipelines in C++ and Python 🔹Hybrid pipelines combining deep learning with classical computer vision for robustness ✅ Deep Learning (ResNet, U-Net, YOLO, MediaPipe, ...) 🔹Custom dataset creation, labeling, and curation 🔹Inference optimization for CPU, GPU, and edge devices 🔹YOLO for real-time object detection, segmentation, OBB, and pose estimation 🔹YOLO fine-tuning and transfer learning for domain-specific data 🔹MediaPipe for pose estimation, hand tracking, face landmarks, and motion analysis ✅ Real-Time & Performance-Critical Software 🔹Low-latency C++ systems optimized for speed and memory 🔹Multithreading, SIMD, profiling, and algorithmic optimization 🔹GPU acceleration (CUDA) when justified 🔹Designed for long-running, production-grade operation under real-world constraints ✅ Embedded & Edge AI 🔹Vision and AI deployment on Raspberry Pi 🔹Sensor integration, control logic, and hardware-in-the-loop testing with ESP32, Arduino ✅ Full Project Ownership 🔹System architecture and technical leadership 🔹Desktop GUIs: Qt, MFC, OpenGL 🔹Clean handover, documentation, and maintainable codebases 👉 Let's discuss how I can bring your AI, computer vision, or embedded system project to life.

I'm a Mechatronics Engineer specialized in automation, control systems, and data-driven engineering solutions. I focus on building real-world systems that combine hardware, software, and intelligent algorithms to solve complex technical problems efficiently and reliably. My experience spans industrial automation, robotics, embedded systems, machine learning, and quantitative modeling. I have developed PLC integrations, predictive algorithms, neural-network forecasting models, and power-electronics control systems, always prioritizing robustness, scalability, and measurable performance improvements. Some highlights of my work include: Designing a full Variable Frequency Drive system with real-time monitoring and ±5% speed accuracy. Building PLC communication architectures across industrial protocols (EtherNet/IP, Modbus, Profibus). Developing machine-learning models for anomaly detection and predictive analysis. Creating automated control tuning software that reduced calibration time and improved production efficiency. Processing hundreds of millions of data points to validate algorithmic systems at scale. I am comfortable working independently on technically demanding projects, communicating clearly with clients, and delivering production-ready solutions. I value clean architecture, well-documented code, and engineering precision. Core skills: Control Systems • PLC Programming • Python • MATLAB • Embedded Systems • Industrial Communications • Machine Learning • Data Analysis • Automation • Robotics • Power Electronics If you are looking for someone who understands both engineering theory and real implementation, I’d be glad to collaborate.

Most computer vision projects fail not in training — but in deployment. Models that hit 95% accuracy in the lab break down when lighting shifts, hardware stutters, or the camera feed isn't clean. I build systems engineered to survive those conditions — and I've done it across industries, hardware platforms, and deployment environments. I'm a Computer Vision Engineer specializing in end-to-end AI pipelines — from raw camera input to real-time inference, deployed on edge hardware, cloud APIs, or both. ━━ Core services ━━ → Object detection & multi-object tracking — YOLOv8, YOLOv5, ByteTrack, BOTSort, MMDetection → Segmentation, pose estimation & keypoints — MediaPipe, custom model architectures → Edge AI deployment — NVIDIA Jetson Orin/Nano, Raspberry Pi, Hailo — TensorRT, ONNX, INT8/FP16 → Cloud & API deployment — FastAPI, Docker, AWS GPU instances, REST & WebSocket inference APIs → Video analytics & smart camera systems — safety monitoring, defect detection, zone tracking, people counting ━━ Systems I've shipped ━━ ✓ Real-time fall detection on NVIDIA Jetson — production-deployed, sub-100ms latency ✓ Zone-based people tracking & monitoring for safety-critical environments ✓ Industrial defect detection pipeline — TensorRT-optimized, running on constrained edge hardware ✓ End-to-end smart camera system: camera → inference → dashboard & real-time alerts ✓ OpenCV video analytics pipelines with custom pre/post-processing and business logic ━━ What makes my work different ━━ Most CV engineers deliver a model file. I deliver a working system — optimized, integrated, and running reliably in your environment. I lead a small team and personally own system architecture, optimization strategy, and core AI engineering on every project. You get senior-level technical execution, not delegation to juniors. Edge or cloud. Jetson or GPU server. Prototype or production scale. I've built across all of it. ━━ How a typical project runs ━━ 1. Discovery — review your hardware targets, data sources, and latency requirements before any code is written 2. Architecture — design the full pipeline: model selection, optimization path, deployment stack, integration points 3. Build & optimize — iterative development with benchmarked FPS and accuracy metrics at each stage 4. Deployment — containerized, documented, and running on your target environment 5. Handover — clean codebase, inline documentation, and a session so your team can maintain it independently ━━ Full tech stack ━━ Models: YOLOv8, YOLOv5, YOLOv7, MMDetection, Detectron2, PyTorch, TensorFlow, ONNX Runtime Tracking: ByteTrack, BOTSort, DeepSORT, StrongSORT, custom zone logic & counting algorithms Optimization: TensorRT INT8/FP16, ONNX quantization, model pruning, batch inference tuning Edge hardware: NVIDIA Jetson Orin/Nano, Raspberry Pi 4/5, Hailo-8, Coral TPU Cloud & infra: FastAPI, Flask, Docker, AWS EC2/Lambda, GCP, RTSP/RTMP stream processing Vision utilities: OpenCV, FFmpeg, GStreamer, PIL/Pillow, custom pipeline components ━━ Project types I take on ━━ → Greenfield CV systems — full pipeline from scratch to production deployment → Model optimization — take an existing model and make it production-fast on your hardware → Edge porting — migrate a cloud-based CV system to Jetson, Raspberry Pi, or Hailo → Pipeline debugging — diagnose and fix latency, accuracy, or stability issues in live systems → Inference API — wrap your CV model as a scalable, low-latency REST or WebSocket API → PoC → production — take a working demo and harden it for real-world deployment at scale → Team augmentation — embedded senior CV engineer for sprints or longer-term engagements ━━ Industries served ━━ Manufacturing & quality control — defect detection, visual inspection, production line monitoring Safety & security — real-time threat detection, perimeter monitoring, crowd analytics Retail & logistics — shelf analytics, people counting, queue management, warehouse tracking Healthcare — patient monitoring support systems, lab automation, medical imaging pipelines Agriculture — crop health detection, drone-based aerial inspection, field monitoring systems ━━ Common questions ━━ Work with our existing dataset? Yes — I assess quality, recommend augmentation strategies, and fine-tune models on your labeled data. Edge or cloud deployment? Both — Jetson, Raspberry Pi, and Hailo at the edge; AWS GPU instances and containerized APIs in the cloud. Can you take our prototype to production? That's one of my most common engagements — hardening, optimizing, and deploying existing concepts for real-world reliability. Documentation and handover included? Always. Clean code, inline comments, deployment instructions, and a dedicated handover session on every project. If you need computer vision that performs beyond lab conditions — on real hardware, with real data, in real-world environments — let's talk.