AI / Machine Learning Engineer (Computer Vision)

@Microlent

Hello, I’ve worked on Python-based machine learning systems where model accuracy and reliability were critical, particularly when dealing with large datasets and real-world deployment constraints. Your goal of building a high-accuracy facial recognition system for a smart lock platform is a challenging but very interesting problem. From your description, the key work will involve developing and training deep learning models for face recognition, improving the computer vision pipeline, and optimizing the system for both edge devices and cloud environments. I’m comfortable working with Python ML stacks such as PyTorch and TensorFlow and building data pipelines for large training datasets. For face recognition problems, approaches based on embedding models like ArcFace or FaceNet combined with strong preprocessing (detection, alignment, normalization) typically produce the best accuracy, especially when optimizing FAR thresholds. I would be happy to share examples of ML and data-driven systems I’ve worked on and discuss how the training pipeline and deployment architecture could be structured for this project. Best, Jenifer

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a High-Accuracy Biometric Authentication System for Smart Locks ❇️ Hi My Friend, I hope you are doing well. I just reviewed your project needs and see you are looking for an AI/ML Engineer. You don’t need to look any further, as Zohaib is here to help you! My team has completed over 50 similar projects focused on biometric systems. I will develop and train machine learning models for facial recognition using large-scale datasets. I will ensure that the system is optimized for both edge devices and cloud systems while maintaining a low false acceptance rate. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in machine learning and computer vision. My expertise includes model training, data analysis, and optimization. Additionally, I have a strong grip on Python, PyTorch, and TensorFlow, ensuring a reliable approach to your needs. ➡️ Let's have a quick chat to discuss your project in detail. I can showcase samples of my previous work and demonstrate how I can add value to your project. Looking forward to discussing with you in chat. ➡️ Skills & Experience: ✅ Machine Learning ✅ Deep Learning ✅ Computer Vision ✅ Facial Recognition ✅ Python Programming ✅ PyTorch ✅ TensorFlow ✅ Dataset Management ✅ Model Optimization ✅ Data Analysis ✅ Identity Verification ✅ Cloud Deployment Waiting for your response! Best Regards, Zohaib

@irsolutions

With over 10 years of experience in AI/ML development, including computer vision projects, I understand the importance of building a high-accuracy biometric authentication system for your smart lock platform. The challenge of achieving an extremely low False Acceptance Rate and accurately identifying users, even in challenging scenarios like identical twins, is a task I am well-equipped to handle. I have successfully delivered AI solutions for various industries, including fintech and healthcare, where accuracy and reliability are paramount. My past projects in computer vision have involved training models with large datasets and optimizing them for real-world deployment, aligning perfectly with your project requirements. I am ready to hit the ground running and start immediately on this exciting project. Please feel free to reach out to discuss how I can contribute to the success of your facial recognition system.

@OutsourceMan

⭐⭐⭐⭐⭐ CnELIndia and Raman Ladhani bring extensive expertise in AI/ML and computer vision, enabling rapid development of high-accuracy facial recognition models. We can design and train deep learning architectures (ArcFace, FaceNet) optimized for extremely low FAR (<0.01%) using large-scale datasets like MegaFace. Proven experience in building robust computer vision pipelines ensures accurate identity verification, even in challenging cases such as identical twins. Expertise in Python, PyTorch, and TensorFlow allows efficient model training, fine-tuning, and deployment across edge devices and cloud systems. We optimize models for real-world deployment, balancing performance with hardware constraints on smart lock platforms. Our team can implement continuous performance monitoring and dataset augmentation to maintain system reliability. Prior experience with hybrid deployment architectures and multimodal AI systems ensures seamless integration with your smart lock ecosystem. Immediate availability ensures the project can start next week and deliver milestones on schedule.

@divumanocha

Hello, Thank you so much for posting this opportunity. It sounds like a great fit, and I’d love to be part of it! I’ve worked on similar projects before, and I’m confident I can bring real value to your project. I’m passionate about what I do and always aim to deliver work that’s not only high-quality but also makes things easier and smoother for my clients. Feel free to take a quick look at my profile to see some of the work I’ve done in the past. If it feels like a good match, I’d be happy to chat further about your project and how I can help bring it to life. I’m available to get started right away and will give this project my full attention from day one. Let’s connect and see how we can make this a success together! Looking forward to hearing from you soon. With Regards!

@faizanmaqbool695

Hello, I’m a software and AI developer with strong experience in Python, Java, machine learning, deep learning, and computer vision applications including facial recognition systems. I can help design scalable software architecture, develop and train AI models, perform data analysis, and integrate models into web or MERN-based applications. I focus on building accurate, efficient, and production-ready AI solutions with clean, maintainable code. I am also experienced in API integration, real-time inference pipelines, and performance optimization. I communicate clearly, follow structured development practices, and deliver on time. Available to start immediately and happy to discuss your project goals, dataset, and deployment requirements.

@pvsysgroupinc

Being a developer with 16 plus years of experience, I have been actively involved in a variety of projects ranging from software development to machine learning. My focus on creating top-performing architectures and implementing the best algorithms makes me confident that I can successfully meet the requirements of your biometric authentication system. My experience spans across data-driven solutions, which gives me a unique edge while working with large datasets - an aspect integral to this project. With my proficiency in Python and Java alongside ML frameworks including Tensorflow and PyTorch, I can adeptly develop and train ML models for face recognition. Having worked on face recognition systems and familiar with models such as ArcFace and FaceNet, I'm equipped with knowledge about tackling intricate challenges like distinguishing identical twins. In addition to these experiences, what sets me apart is my commitment to your project as a solo operator. Being the owner of my company for 33 years, you'll be dealing directly with me throughout the project's duration. That means better communication, efficient implementation, and greater ownership. As we both value immediate project initiation, let's launch this endeavor on a strong note together!

@Julian29

I HAVE SUCCESSFULLY DEVELOPED HIGH-PRECISION FACIAL RECOGNITION SYSTEMS WITH LOW FAR FOR SECURE AUTHENTICATION – READY TO DELIVER NEXT-GEN SMART LOCK SOLUTIONS! I will build a robust face recognition platform capable of distinguishing even challenging cases like identical twins, with FAR <0.01%. Core features include secure user enrollment, real-time identity verification, role-based access control, and seamless cloud-edge integration. Users will have intuitive interfaces for management, while admins can monitor and control access efficiently. I will provide full source code and 2 years of free ongoing support post-launch to ensure smooth operation and updates. My expertise spans large-scale dataset training (e.g., MegaFace), optimized deep learning models using PyTorch/TensorFlow, and deployment on both edge devices and cloud. I am committed to delivering a reliable, scalable, and highly secure system that exceeds your expectations. Relevant past projects and portfolio will demonstrate my proven results in similar AI-driven authentication systems.

@vishal1145

Your FAR target of <0.01% on identical twins is achievable, but only if you're using triplet loss with hard negative mining and augmenting your training data with synthetic twin pairs. Most off-the-shelf ArcFace implementations will fail this test because they weren't trained on genetically similar faces. Before architecting the solution, I need clarity on two things - what's your edge device compute budget (can you run a 50MB model with 100ms inference time, or do you need sub-20MB with real-time processing)? And are you planning active liveness detection (blink/head movement) or passive (texture analysis), because that changes the entire pipeline architecture? Here's the architectural approach: - ARCFACE + PYTORCH: Fine-tune ArcFace on MegaFace with custom triplet mining that specifically targets high-similarity pairs, then distill the model down to 30MB for edge deployment while maintaining <0.01% FAR. - COMPUTER VISION PIPELINE: Build a multi-stage verification system - face detection (MTCNN), alignment (facial landmarks), anti-spoofing (texture CNN), then embedding extraction with cosine similarity thresholding at 0.85+ for twin-level accuracy. - EDGE OPTIMIZATION: Convert trained models to TensorFlow Lite with INT8 quantization, achieving 80ms inference on ARM processors without sacrificing accuracy by using knowledge distillation from the full-precision cloud model. - HYBRID DEPLOYMENT: Design a fallback architecture where edge devices handle 95% of verifications locally, but flag low-confidence cases (similarity 0.75-0.85) to the cloud for ensemble verification using multiple model checkpoints. I've built similar biometric systems for two fintech clients that needed sub-0.1% FAR for KYC compliance. Let's schedule a 15-minute technical call to discuss your liveness detection requirements and hardware constraints before you commit to a build - I don't take on projects where the edge compute specs are undefined.

@MQamar123

As a seasoned AI / ML Engineer, I offer a unique combination of experience and adaptabilitythat make me the perfect fit for your biometric authentication system project. With a strong foundation in Machine Learning and Deep Learning, as well as hands-on capability in Computer Vision, I've honed my craft over 7 years to help me navigate complex projects like the one you're offering. Especially relevant points from my skill-stack for your project include expertise on Python (PyTorch and TensorFlow), Large-scale dataset training and management and much more. One thing that sets me apart from others is my extensive background developing and deploying AI solutions for real-world applications, including working with edge devices paired with cloud systems - which is precisely the hybrid environment you seek. Another strength I bring to the table is my experience working with high stake projects where precision and accuracy are paramount,

@mykhaylob4

✋ Hi there. I can develop your high-accuracy facial recognition system for smart locks, ensuring extremely low false acceptance rates and reliable performance even in challenging scenarios like identical twins. ✔️ I have strong experience in machine learning and computer vision, with hands-on work in Python using PyTorch and TensorFlow. I have trained models on large-scale biometric datasets, built optimized pipelines for identity verification, and deployed solutions both on edge devices and cloud environments. In past projects, I focused on improving recognition accuracy, handling large datasets, and maintaining efficient inference for real-world applications. ✔️ For your project, I will develop and train deep learning models for face recognition, design robust computer vision pipelines, and optimize them for edge and cloud deployment. I will ensure the system performs reliably under all lighting and angle variations, and I can provide testing and validation on your target hardware. Documentation and guidance for deployment will be included. ✔️ I am ready to start immediately and can align with your timeline for a contract-based engagement. We can discuss dataset access, deployment environment, and model optimization strategies before kick-off. Best regards, Mykhaylo

@mughiraaa

hi! i have reviewed the details of your project and i can do this!!. we have handled similar projects successfully, and I am confident we can deliver high quality results for you. i will first understand exactly what you need, then plan everything step by step to make sure the work runs smoothly. we prefer clear communication and regular updates so that the project progresses smoothly and meets your expectations. let's have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. I will share my portfolio in the chat to show relevant examples of our past work. looking forward to your response. mughiraa

@Aaleen110

Hi there! I noticed your need for a dedicated Computer Vision engineer and immediately thought of my recent experience architecting real-time object tracking and segmentation systems. Having successfully deployed high-performance ML models for diverse industrial applications, I am well-versed in the specific challenges of optimizing vision pipelines for both accuracy and production-level throughput. My background focuses on bridging the gap between research-grade algorithms and scalable, reliable production code, ensuring that the final output is both technically sound and commercially viable. To tackle this project, I would begin by evaluating your current dataset to implement advanced augmentation strategies using Albumentations or OpenCV, ensuring the model generalizes well to edge cases. My technical preference typically involves fine-tuning SOTA architectures like YOLOv10 or specialized Vision Transformers within a PyTorch or TensorFlow framework, followed by model compression via ONNX or TensorRT for low-latency inference. I integrate robust CI/CD pipelines for ML (MLOps) using Docker and DVC to maintain strict version control and ensure that the transition from training to deployment is seamless, reproducible, and easily monitored for feature drift. Is there a specific deployment environment or hardware target—such as Jetson edge devices or a cloud-based Kubernetes cluster—that we need to optimize for? Additionally, are we looking to improve an existing model's performance or build a custom architecture from the ground up? I would love to hop on a quick call or exchange a few messages to dive deeper into these requirements and align on a technical strategy that meets your goals.

@AhmadXpert2410

Hi there, I can contribute to building your high-accuracy biometric authentication system with a focus on extremely low False Acceptance Rate (FAR < 0.01%). With hands-on experience in deep learning and computer vision, I have developed and optimized facial recognition pipelines using Python and frameworks like PyTorch and TensorFlow, working with large-scale datasets to improve model accuracy and robustness—even under challenging scenarios like twins or varied lighting conditions. My approach will include training and fine-tuning models such as ArcFace or FaceNet, implementing efficient preprocessing pipelines, and optimizing for hybrid deployment on both edge devices and cloud environments. I will ensure the system is reliable, performant, and scalable, maintaining strict standards for identity verification while minimizing errors. I can also provide guidance on dataset management, model evaluation, and deployment best practices, ensuring seamless integration with your smart lock platform. With immediate availability, I am ready to start next week and support rapid, high-quality delivery for your project. Regards, Ahmad

@AlexVydrin

Hello, Your project aligns well with my experience developing computer vision and machine learning systems that focus on high accuracy identity verification. I have worked with deep learning frameworks such as PyTorch and TensorFlow to train and optimize models for recognition tasks, including pipelines that process large biometric datasets and deploy models across both cloud and edge environments. I am familiar with face recognition approaches such as ArcFace style embeddings and techniques for reducing false acceptance rates while maintaining real time performance. I would be glad to contribute to building a reliable biometric authentication system for your smart lock platform. Best regards.

@rbtech1984

Hello There!!! ★★★★ ( AI / Machine Learning Engineer - Computer Vision ) ★★★★ I understand you need a high-accuracy facial recognition system for a smart lock, capable of FAR <0.01% and reliable even with identical twins. The project involves training models on large-scale biometric datasets, optimizing pipelines for both edge devices and cloud, and integrating a robust CV system. ⚜ Develop and train deep learning face recognition models ⚜ Build and optimize computer vision pipelines ⚜ Handle large-scale biometric datasets for accuracy improvement ⚜ Deploy models to edge devices and cloud systems ⚜ Integrate ArcFace/FaceNet or similar architectures ⚜ Optimize performance and latency for real-world use ⚜ Ensure security and reliability in identity verification I have strong Python, PyTorch/TensorFlow skills, and prior experience building facial recognition systems and CV pipelines. I’ll structure the project with data preprocessing, model training, evaluation, and deployment phases, ensuring clean, maintainable code. Excited to start immediately and deliver production-ready results. Warm Regards, Farhin B.

@Shaiwan

❗❕‼️⁉️ Hello ⁉️‼️❕❗ I understand you need a high-accuracy facial recognition system for smart locks with FAR < 0.01%, deployable on edge devices and cloud. I HAVE SOME QUESTIONS REGARDING THE PROJECT SEND ME A MESSAGE FOR MORE DISCUSSION ❗❕❗❕❗❕ ⇆ ⇆ ⇆ ➷ Develop and train deep learning models using PyTorch/TensorFlow on MegaFace and mobile datasets ➷ Build computer vision pipelines for identity verification, including ArcFace/FaceNet integration ➷ Optimize models for real-time deployment on edge devices and cloud, ensuring low latency and high accuracy ⇆ ⇆ ⇆ I am best suited with 7+ years in AI/ML, computer vision, and large-scale biometric systems. Approach: analyze datasets, design model architecture, train and validate for accuracy, optimize for deployment, implement edge-cloud pipeline. Let’s chat to discuss project requirements and start immediately. Best Regards, Shaiwan Sheikh

@krisk63

Hi there, I’m Kristopher Kramer from McKinney, Texas. I’ve worked on similar projects before, and with over 15 years of experience as a senior full-stack and AI engineer, I have the expertise to deliver this properly. I’m available to start right away and would be happy to discuss the details whenever it’s convenient for you. I look forward to speaking with you. Best regards, Kristopher Kramer

@Visharaa2024

I am an experienced AI/ML engineer with strong expertise in computer vision and deep learning, specializing in building high-accuracy facial recognition systems. I have hands-on experience training models on large-scale datasets such as MegaFace and optimizing them for deployment on both edge devices and cloud systems. I am proficient in Python, PyTorch, and TensorFlow, and familiar with architectures like ArcFace and FaceNet. I have worked on challenging identity verification problems, including distinguishing visually similar individuals, and have integrated AI models into real-world applications. I am available to start immediately and am confident in delivering a robust biometric authentication solution with extremely low false acceptance rates for your smart lock platform.