Machine Learning Engineer - Python/Tensorflow

Vayuz Technologies is seeking a highly skilled and experienced Machine Learning Engineer with expertise in Python and TensorFlow to join our innovative team. You will be instrumental in developing, optimizing, and deploying sophisticated ML and Deep Learning models, working with complex datasets, and ensuring the reliability and performance of our AI-driven solutions in production environments.

Key Responsibilities

• Data Analysis & Feature Engineering: Conduct comprehensive feature engineering, data analysis, and data exploration to extract valuable insights from diverse datasets.
• Model Development & Optimization: Develop and optimize cutting-edge Machine Learning models to achieve high accuracy, efficiency, and performance.
• Deep Learning Implementation: Design and implement advanced Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques.
• Imbalanced Data Handling: Effectively handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness, generalization, and robustness.
• Model Deployment & Monitoring: Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on real-world feedback and performance metrics.
• Cross-Functional Collaboration: Collaborate seamlessly with cross-functional teams, including data scientists, software engineers, and product managers, to align ML solutions with overarching business goals.
• Statistical & Mathematical Application: Utilize fundamental statistical knowledge and strong mathematical principles to ensure the reliability, validity, and interpretability of models.
• Innovation & Research: Stay abreast of the latest advancements in ML and AI, actively bringing in new techniques and research findings to drive innovation within the team and company.

Requirements

• Hands-on experience in Machine Learning and Deep Learning.
• Strong expertise in feature engineering, data exploration, and data preprocessing.
• Experience with imbalanced datasets and techniques to improve model generalization.
• Proficiency in Python, TensorFlow, Scikit-learn, and other relevant ML frameworks and libraries.
• Strong mathematical and statistical knowledge coupled with excellent problem-solving skills.
• Ability to optimize models for high accuracy and performance in real-world scenarios.

Preferred Qualifications

• Experience with Big Data technologies (e.g., Hadoop, Spark).
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Experience in automating ML pipelines with MLOps practices.
• Experience in model deployment using cloud platforms (e.g., AWS, GCP, Azure) or MLOps tools.