Machine Learning Engineer Computer Vision

As a Machine Learning Engineer specializing in Computer Vision with 4+ years of experience, you will be responsible for designing, training, optimizing, and deploying high-performance ML models with a strong focus on maximizing model accuracy and performance for production-grade systems. Your role will involve working extensively with deep learning frameworks such as PyTorch and TensorFlow, implementing advanced techniques for model optimization, hyperparameter tuning, data augmentation, error analysis, and performance benchmarking. Additionally, you will be required to improve inference performance using NVIDIA TensorRT, CUDA, and GPU acceleration, as well as converting research models into production-ready pipelines to continuously enhance model accuracy, latency, and resource efficiency. Your key responsibilities will include: - Designing, training, and fine-tuning Computer Vision models for high accuracy and robustness - Implementing advanced techniques for model optimization, hyperparameter tuning, data augmentation, error analysis, and performance benchmarking - Improving inference performance using NVIDIA TensorRT, CUDA, and GPU acceleration - Converting research models into production-ready pipelines - Collaborating closely with backend, product, and data teams to align ML outputs with business goals In addition to the core responsibilities, it would be beneficial if you have hands-on DevOps exposure, experience with Docker and containerized ML workloads, a basic understanding of CI/CD pipelines for ML models, familiarity with cloud platforms (AWS/GCP/Azure), and experience in model monitoring, logging, and performance tracking in production. Exposure to Kubernetes, MLflow, or similar MLOps tools is also considered a plus. Required Skills & Qualifications: Must-Have - 4+ years of hands-on experience in Machine Learning & Computer Vision - Strong understanding of CNNs, object detection, segmentation, image classification - Experience with NVIDIA TensorRT, GPU acceleration, and model inference optimization - Proficiency in Python and ML frameworks (PyTorch / TensorFlow) - Solid understanding of data preprocessing and feature engineering Good to Have - 1+ year of DevOps or MLOps experience - Experience deploying ML models in production - Knowledge of cloud infrastructure and scalable systems - Familiarity with Linux environments and scripting In this role, you will have the opportunity to work on high-impact Computer Vision problems, push model accuracy to industry-leading levels, gain exposure to production-scale ML and real-world deployment challenges, and be part of a collaborative engineering culture with room for growth. Froots Technologies is an agri-tech innovation company focusing on building cutting-edge AI solutions that bring quality intelligence, traceability, and digitization into agricultural value chains. By blending Computer Vision, ML, and real-world deployment, the company aims to solve high-impact problems at scale. If you are passionate about pushing AI models from research to rugged, real-world environments, this is the ideal place for you to grow and excel. As a Machine Learning Engineer specializing in Computer Vision with 4+ years of experience, you will be responsible for designing, training, optimizing, and deploying high-performance ML models with a strong focus on maximizing model accuracy and performance for production-grade systems. Your role will involve working extensively with deep learning frameworks such as PyTorch and TensorFlow, implementing advanced techniques for model optimization, hyperparameter tuning, data augmentation, error analysis, and performance benchmarking. Additionally, you will be required to improve inference performance using NVIDIA TensorRT, CUDA, and GPU acceleration, as well as converting research models into production-ready pipelines to continuously enhance model accuracy, latency, and resource efficiency. Your key responsibilities will include: - Designing, training, and fine-tuning Computer Vision models for high accuracy and robustness - Implementing advanced techniques for model optimization, hyperparameter tuning, data augmentation, error analysis, and performance benchmarking - Improving inference performance using NVIDIA TensorRT, CUDA, and GPU acceleration - Converting research models into production-ready pipelines - Collaborating closely with backend, product, and data teams to align ML outputs with business goals In addition to the core responsibilities, it would be beneficial if you have hands-on DevOps exposure, experience with Docker and containerized ML workloads, a basic understanding of CI/CD pipelines for ML models, familiarity with cloud platforms (AWS/GCP/Azure), and experience in model monitoring, logging, and performance tracking in production. Exposure to Kubernetes, MLflow, or similar MLOps tools is also considered a plus. Required Skills & Qualifications: Must-Have - 4+ years of hands-on experience in Machine