Machine Learning Engineer (Python Developer)

As a Machine Learning Engineer, you will play a crucial role in designing, developing, and deploying machine learning models that power various applications such as predictive analytics, natural language processing, computer vision, recommendation systems, and more. Your primary responsibility will involve close collaboration with data scientists, software engineers, and product teams to create scalable and high-performance AI solutions. Your expertise should encompass a solid foundation in machine learning algorithms, data processing, and software development. You must be adept at taking charge of the entire machine learning lifecycle, starting from data collection and model training to deployment and monitoring. Your key responsibilities will include designing and implementing machine learning models for diverse applications, working with large datasets for data preprocessing, feature engineering, and data augmentation, training, optimizing, and fine-tuning models using modern machine learning frameworks, deploying models into production environments using tools like Docker, Kubernetes, or cloud platforms, continuous monitoring of model performance, accuracy, and scalability, integrating machine learning solutions into business workflows by collaborating cross-functionally, and staying updated on the latest trends and advancements in machine learning and AI to foster innovation within the organization. To qualify for this role, you should hold a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field. While a Ph.D. is advantageous, it is not mandatory. Additionally, you should ideally have 0-1 years of experience in machine learning, data science, or a related technical role, with a proven track record of building and deploying machine learning models in production environments and familiarity with cloud platforms like AWS, GCP, or Azure for model deployment and scalability. Your technical skills should include proficiency in Python (preferred) or other programming languages like R, Java, or C++, strong knowledge of machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, or similar, familiarity with deep learning techniques like CNNs, RNNs, transformers, experience with data processing tools such as Pandas, NumPy, and SQL, knowledge of version control (e.g., Git), containerization (e.g., Docker), and CI/CD pipelines, and experience with big data tools and frameworks like Hadoop and Spark would be advantageous. In addition to technical skills, soft skills such as strong problem-solving and analytical abilities, excellent communication and collaboration skills, the capacity to work independently while managing multiple priorities in a fast-paced environment, attention to detail, organization, and a passion for learning and innovation are essential for success in this position. This is a full-time position with a day shift schedule and requires in-person work at the designated location.,