Senior Embedded Controls Engineer: C++/Linux and Machine Learning exp. As an AI Machine Learning Engineer focus will be on designing and developing scalable solutions using AI tools and machine learning models. Addressing various neural network-related challenges in transportation sector. This involves leveraging big data computation and storage tools to create prototypes and datasets, conducting model training and evaluations, integrating solutions, performing bench tests and onsite tests, tuning, and monitoring. Proficiency in languages such as C and C++ is required, along with software development for Linux platforms. Your responsibilities Design and develop real time AI ? Neural Network solutions for transportation industry maintenance equipment. Implementing appropriate ML algorithms. Write clean, documented code following best practices. Develop and implement communication protocols. Work independently and collaboratively with a motivated team. Generate requirements and design documentation. Plan for, design, and deliver testing, and tested products into the QA process. Apply communication and problem-solving skills to solve software issues related to the design, development, deployment, testing, and operation of systems.
QualificationsEducationMaster"s / Bachelor"s degree in Software Engineering or similar experience.Experience 5+ years of experience in developing CNN, R-CNN type neural network for computer vision tasks. 5+ years of experience in Software development using C++ & Linux embedded. Experience with Supervised and Semi-Supervised Learning, Deep Learning, Support Vector Machines, Linear and Logistic Regression. Working knowledge of AI Framework such as TensorFlow, Caf?, PyTorch, Keras, Darknet and OpenCV. Working knowledge of AI edge devices such as NVIDIA Jetson / Nano / Orin. Knowledge of the Linux Operating System.Preferred Experience Experience using statistical computer languages (R, Python, SQL etc.) to manipulate data and draw insights from large data sets. Experience working with and creating data architectures. Knowledge of a variety of machine learning techniques (semantic segmentation, clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks. Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications. Experience with edge computing & controlling devices (On-device deployment in C/C++ or similar) for real time application. Experience with optimizing neural networks to perform well on low-power mobile platforms (e.g. pruning, distillation, quantization).