Spydra - Machine Learning Engineer - R/Python/Java

Job Description

Job Summary We are seeking a talented and motivated Machine Learning Engineer to join our team. The ideal candidate will have a strong background in machine learning algorithms, data analysis, and software development. You will be responsible for designing, developing, and deploying machine learning models and systems that drive our products and services. Key Responsibilities Develop and implement machine learning algorithms and models. Design and conduct experiments to evaluate the performance of machine learning models. Collaborate with cross-functional teams to integrate machine learning solutions into products. Analyze large datasets to extract meaningful insights and patterns. Optimize and tune machine learning models for performance and scalability. Monitor and maintain deployed models, ensuring their performance in a production environment. Stay up-to-date with the latest trends and advancements in machine learning and artificial intelligence. Write clean, maintainable, and efficient code. Document processes, experiments, and results : Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. Proven experience as a Machine Learning Engineer or similar role. Strong understanding of machine learning algorithms and techniques (e.g., supervised and unsupervised learning, reinforcement learning, deep learning). Proficiency in programming languages such as Python, R, or Java. Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Familiarity with data preprocessing and feature engineering techniques. Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) is a plus. Strong problem-solving skills and the ability to work independently and as part of a team. Excellent communication and collaboration skills. Preferred Qualifications Experience with natural language processing (NLP) and computer vision. Knowledge of big data technologies (e.g., Hadoop, Spark). Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes). Experience with version control systems (e.g., Git). Understanding of DevOps practices and CI/CD pipelines. (ref:hirist.tech)