ML Engineer

As a Machine Learning Engineer, you will be applying your expertise to help develop a world-leading capability in the domain of machine learning. Your responsibilities will include contributing to the design, development, deployment, testing, maintenance, and enhancement of machine learning software solutions. You will work on applying machine learning, deep learning, and signal processing techniques on large datasets such as audio, sensors, images, videos, and text to develop models. Additionally, you will be involved in architecting large scale data analytics and modeling systems, designing and programming machine learning methods, and integrating them into the ML framework and pipeline. Collaboration with data scientists and analysts to support the development of ML data pipelines, platforms, and infrastructure will be a key aspect of your role. You will evaluate and validate analyses using statistical methods and present findings in a clear manner to individuals not familiar with data science or computer science. Furthermore, you will create microservices and APIs for serving ML models and services, evaluate new machine learning methods, and perform feature engineering to enhance model performance. The required skills for this role include a background and knowledge of recent advances in machine learning, deep learning, natural language processing, and image/signal/video processing, with around 5 years of professional work experience in real-world applications. Proficiency in programming languages such as Python, Pytorch, MATLAB, C/C++, and Java, along with knowledge of software engineering concepts like OOP and design patterns, is essential. Familiarity with machine learning libraries such as Tensorflow, Keras, scikit-learn, and pyTorch is also required. A strong mathematical background with skills in accuracy, significance tests, visualization, and advanced probability concepts is necessary. You should be capable of architecting and implementing end-to-end solutions for accelerating experimentation and model building, as well as possessing knowledge of various machine learning techniques such as clustering, decision tree learning, and artificial neural networks. The ability to conduct both independent and collaborative research, along with excellent written and spoken communication skills, is crucial. Preferred qualifications include a B.E./B.Tech/B.S. degree with 3+ years of experience in the relevant fields or an M.E./M.S./M.Tech/PhD, preferably in Computer Science, with experience in machine learning, image and signal processing, or statistics.,