Deep Learning Jobs

We are looking for an experienced Senior AI/ML Engineer to join our team and help build advanced AI-driven solutions. The ideal candidate will have a strong background in machine learning, deep learning, and data science, with expertise in deploying models at scale. If you're passionate about tackling complex challenges and working with cutting-edge AI technologies, we want to hear from you! We are looking for an individual, not agency. Scope of work - Collaborate with the team to integrate AI/ML technologies into current projects. - Develop and optimize machine learning algorithms. - Perform ongoing analysis and validation of AI models. Key Responsibilities: ** Fluent English Skill is MUST!!! So if you don't have ENGLISH SKILLs don't apply for this position. Develop a...

Project Title: MERN Stack Web Application Development Project Description: I am looking for a developer who can build a web application using the MERN Stack (MongoDB, , React.js, Node.js). This project is for academic purposes. Requirements: • Frontend: React.js with responsive UI • Backend: Node.js and • Database: MongoDB • Features: User authentication (login/signup), CRUD operations, basic dashboard • Clean and well-structured code Additional Requirements: • Source code must be provided • Basic documentation of the project • Proper comments in the code Budget: Open for discussion Deadline: 5–7 days If you have experience with MERN Stack development, please place your bid and share your previous work if available.

**Project Title:** Source Detection using Human Behaviour Dynamics using Machine Learning / Deep Learning / AI **Project Description:** I am looking for an experienced Machine Learning / Deep Learning developer to build a model also novelty is must **Scope of Work:** * Dataset preprocessing and exploration * Model development using ML/DL techniques * Training and testing the models * Performance evaluation (Accuracy, Precision, Recall, Confusion Matrix) * Comparison of different models * Clear documentation of the workflow **Preferred Tech Stack:** * Python * Machine Learning / Deep Learning models * TensorFlow / PyTorch / OpenCV * Data visualization libraries **Deliverables:** * Complete source code * Trained model * Results and performance comparison * Brief documentation of th...

Project Overview Designed and implemented an intelligent DDoS detection and mitigation framework for Software-Defined Networking (SDN) based Vehicular Ad-hoc Networks (VANETs) to ensure secure, reliable, and low-latency vehicle-to-infrastructure (V2I) communication. The system leverages SDN’s centralized control to dynamically monitor network traffic and mitigate DDoS attacks in real time, ensuring uninterrupted safety-critical vehicular services. Key Innovations: SDN-Based Centralized Security Control OpenFlow-enabled SDN controller Global view of VANET traffic behavior Intelligent DDoS Detection Flow-level traffic analysis Anomaly detection using ML / Deep Learning (LSTM Autoencoder / Hybrid model) Real-Time Mitigation Strategy Malicious vehicle flow isolation Dynamic ru...

Explainable AI Models for Any topic with novelty Project Description: Build a complete end-to-end Machine Learning project using dataset (CSV) to identify the source of infection using Explainable AI models. The project should include data loading, EDA (missing values, distributions, correlations, visualizations), data preprocessing (handling missing data, encoding categorical features, scaling numeric data, removing outliers), and feature selection. Train and compare multiple ML models with cross-validation and hyperparameter tuning, and include explainability using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). Deploy the system with a Streamlit web UI where users can upload datasets, select the target column, run preprocessing, train...

I’m working through an year long online course on Artificial Intelligence and Machine Learning and I’m running short on time for several graded assignments and mini-projects. Rather than general tutoring or technical platform troubleshooting, I specifically need someone who can step in, finish the required tasks, and submit clean, well-commented solutions on my behalf. This will be an ongoing project and will span an year. Typical exercises range from data preprocessing and feature engineering to training and evaluating models with libraries such as Python, NumPy, pandas, scikit-learn, TensorFlow or PyTorch. Code must run flawlessly in the course’s Jupyter-based environment and meet the rubric laid out in each brief (accuracy thresholds, narrative explanations, and any...

Tôi cần một chuyên gia hỗ trợ xây dựng model phân tách neutron/gamma bằng Deep Learning sử dụng Python. Phạm vi công việc • Phân tích yêu cầu và đề xuất kiến trúc mạng phù hợp (CNN, RNN, hoặc hybrid nếu cần). • Xử lý và chuẩn hóa dữ liệu đầu vào; nếu thiếu dữ liệu, hỗ trợ tạo hoặc mở rộng bộ dữ liệu mô phỏng. • Huấn luyện, đánh giá và tinh chỉnh mô hình để đạt độ chính xác tối ưu. • Bàn giao mã nguồn Python kèm hướng dẫn chạy, file trọng số đã huấn luyện và báo cáo tóm tắt kết quả. Kỳ vọng • Ưu tiên giải pháp gọn nhẹ, dễ t&aac...

I need a complete, reproducible deep-learning pipeline that takes raw leaf images, learns to recognise plant diseases, and then serves the prediction through a Streamlit interface. Because I do not yet have the images, the first task is to identify and download a suitable, well-labelled dataset from Kaggle. Feel free to compare a few candidates, but the final choice should give good class balance and enough samples per disease category. Once the data is in place, walk through exploratory data analysis, preprocessing, and augmentation inside a Jupyter notebook. From there, build and tune a convolutional neural network (TensorFlow / Keras or PyTorch are both fine) and report the usual metrics plus a confusion matrix so I can judge class-wise performance. When the model is satisfactory, save...

SCRM dashboard: Track victims, chat logs, stages (hunt → raise → kill), message history. • AI chat automation: LLM-powered bot (ChatGPT/Claude style) with pre-set grooming scripts for rapport, flirting, daily check-ins, objection handling, profit teasing, small deposit pushes. Auto 70-80% of early chats; manual takeover for big targets. • Fake crypto trading platform: Web + mobile app (React/Flutter). User side: login, fake wallet, live-looking charts, trade history, auto-show rising profits (2-10% daily). Admin side: control all user balances, see real deposits (crypto wallets), manually adjust fake gains, block withdrawals. • Bulk outreach basics: Tools/scripts for wrong-number SMS blasts, dating app auto-messages, profile generator (AI photos + bios). • ...

I have continuous video footage from live inspection of steel flat bars strips our production line and need a complete deep-learning pipeline that flags surface, structural, and functional defects in real time. The raw videos are already labeled by timestamp; frame-level annotation may still be required for optimal accuracy. Scope • Design and train a deep neural network—CNN, transformer, or hybrid model—that detects all three defect categories directly from video streams. • Implement preprocessing (frame extraction, augmentation, ROI isolation) and post-processing (tracking, alert generation) in Python using libraries such as PyTorch/TensorFlow and OpenCV. • Optimise for inference on an on-premise GPU; latency under 200 ms per frame is the target. &bull...

Build an end-to-end AI application that can reliably solve JEE-style math problems, explain solutions step-by-step, and improve over time. The goal of this assignment is not just model usage, but to evaluate whether you can: design a RAG pipeline build a multi-agent system handle image, text, and audio inputs introduce human-in-the-loop (HITL) implement memory & self-learning package everything into a working application and deploy it You do not need to be a DevOps or full-stack expert, but you must be able to build, run, and deploy a simple app.

project is to develop an image processing–based infrared small target detection system that identifies small targets in infrared images using a combination of local contrast analysis and nonlocal spatial information. The project will use a publicly available dataset The project will involve implementing and integrating two classical image processing methods: A. Dual-Window Local Contrast Method (DW-LCM) B. Multiscale Window Infrared Patch-Image Model (MW-IPI)

I’m building a ROS 2 pipeline that must reliably spot nuts and bolts in a live camera feed and publish their positions to the rest of my stack in real time. Your job is to create the complete vision-detection module—from model training or fine-tuning through to a clean ROS 2 node that subscribes to an image topic and spits out the detected objects with bounding boxes (or masks) and a confidence score. OpenCV, TensorFlow/PyTorch and any of the common ROS 2 image-transport plugins are all fine as long as the final node runs on Humble and stays GPU-agnostic (CUDA acceleration is a bonus, not a requirement). I already have a test rig with a standard USB camera; if you need specific calibration images I can capture them for you. Please deliver: • Source code for the detection...

More details: Which deep learning framework do you prefer for this project? TensorFlow Do you have a preferred dataset for brain MRI image segmentation? Please use a publicly available dataset Which style of output visualization do you prefer? 2D slices with segmentation overlay

I’m building a virtual DeepseekV3 environment that emulates Jet Nano hardware for research and development on machine-learning models. The goal is to give my team a sandbox where we can move seamlessly from data preprocessing and feature extraction through model training, evaluation, deployment, and monitoring—without touching the physical board until we are ready. Here’s what I need: • A reproducible simulation that mirrors Jet Nano’s CUDA-enabled GPU, memory constraints, and I/O. • Containerised tool-chain (PyTorch, TensorRT, cuDNN, etc.) with scripts that cover the full life-cycle: preprocessing, training, hyper-parameter sweeps, evaluation metrics, and a mock-deployment stage that tracks resource usage and latency. • Clear documentation so a...

Real-Time Face Swap System Requirements Specification 1. Project Overview Purpose: Development of real-time face replacement functionality aimed at protecting privacy and expanding expressive capabilities during streaming. Users: Exclusively for individual streamers. Streaming Environment: - Current: Mac mini (M4 chip, 16GB RAM) - Future: Upgradeable to 32GB/64GB as needed - OS: Mac preferred (portability priority), Windows compatibility considered if necessary - Streaming Software: OBS to be used 2. Non-Negotiable Requirements (Must Have) 2-1. Technical Requirements Face Replacement Specifications: - Replace only eyes and eyebrows - Use original parts for nose down (mouth/lips) - Reason: To prevent double lips when drinking liquids Safety: - Face processing must never drop out w...

I’m building a diffusion–based pipeline focused on image generation, and I want the results to be crisp, large-format visuals rather than the low-res outputs many models settle for. Your task is two-fold: first, curate or assemble a fit-for-purpose dataset (I don’t yet have one), and second, train a state-of-the-art z-image diffusion model that consistently produces high-resolution renders. You’ll have freedom in tool choice—PyTorch, TensorFlow, DreamBooth, LoRA or any other modern techniques are fine—as long as the final model can be reproduced from the training scripts you deliver. I expect the usual artefacts: cleaned dataset (with clear licensing notes), training code, model checkpoints, and a concise README outlining hyper-parameters, compute used,...

1. Real-time Mesh Proximity Architecture Description: Establish a proximity-based mesh positioning network between the Master device (Coordinator) and Member devices (Nodes). Technical Approach: Utilize Bluetooth Low Energy (BLE) and Wi-Fi Direct to estimate relative distance. Requirements: The Master device must continuously receive signals from Nodes. If the RSSI (Received Signal Strength Indicator) of a Node drops below a predefined threshold (corresponding to a configured physical distance), the Master must trigger an alert notification. 2. Zero-touch Check-in Authentication Description: The system automatically scans and confirms the presence of Nodes within connection range. Technical Approach: The Master broadcasts a multicast packet via local network or Bluetooth. Nodes r...

We are looking for a developer who can build Remotion-based video templates similar in style and complexity to the attached example animation. The key requirement is that each template must be fully configurable via JSON inputs, so we can programmatically render different versions of the same motion graphic. You will be responsible for: Creating reusable Remotion compositions using React and TypeScript Exposing clear, well-documented JSON props (text, colors, media, timing, layout options, etc.) Ensuring smooth, modern motion graphics suitable for marketing and social media content Optimizing for reliable rendering in an automated pipeline (no manual timeline tweaking) We will handle: Providing example animations and reference designs Supplying JSON input formats and data structure...

I need a compact, fast object-detection model that runs directly on an edge board (Jetson Nano, Raspberry Pi, Coral or similar) and processes aerial images from drones. The immediate application is surveillance, yet the solution should stay flexible enough to be reused later in agriculture or disaster-response scenarios. Source imagery will contain a mix of people, cars, buses, bicycles and assorted infrastructure. The model must be especially reliable at spotting people and critical infrastructure elements, while still recognising the wider vehicle classes.I am open to any justified architecture (YOLOv8, MobileNet-SSD, EfficientDet-Lite, or superior alternatives)—provided it outperforms YOLOv9c and similar models while delivering real-time inference on edge devices once quantized o...

We are seeking an AI lead developer to build AI orchestration workflows and integration of various agents like: - DevOps Agent - Analytics Agent - Dashboard Agent - Testing Agent Requirements: - Provide an architecture for generating real-time dashboards from unstructured logs from microservices in Kubernetes - Experience with data engineering and AI skills - Unstructured data stored as logs in JSON format in S3 bucket in AWS - Real-time data in terabytes - Long-term availability for this project - Build cursor-like frontend for analytics - Experience in UI development is a plus Skills needed: AI App Development, Machine Learning, AI Code Generator, AI Agent Development, Deep Learning, RAG, Python, Node.js, TypeScript

Tenemos nuestro MVP 0 corriendo en Railway con un front en React + TypeScript y un backend Node.js que consume Gemini API, pero con bugs críticos y los agentes de IA aún no alcanzan la precisión que necesitamos para presentarnos con seguridad ante inversores pre-seed. Mi prioridad ahora es aumentar su exactitud sin comprometer la velocidad ni la estabilidad del resto del sistema.   Necesito a alguien que entre al código, mejore el razonamiento de los agentes, afine los prompts y ajuste cualquier parte del pipeline de inferencia que hoy genere resultados inconsistentes.   No es requisito, pero se agradece si puedo ver en tu propuesta ejemplos concretos de trabajos previos donde hayas incrementado la precisión de modelos o agentes IA en producci&o...

The project centers on building a production-ready TensorFlow 2.x model that classifies tabular data delivered to us through an internal API. I have the API specifications and sample payloads ready; you will turn those streams into a clean training pipeline, engineer the right features, and iterate until the classifier meets our performance targets in real-world tests. Scope of work • Data pipeline – pull the API data, handle preprocessing, and produce TensorFlow-friendly datasets for train/val/test splits. • Model development – design, train, and tune a deep learning architecture suitable for tabular inputs (e.g., wide & deep, Transformer, or other proven structures). • Optimization – experiment with hyperparameters, regularization, and callback...