Machine Learning Engineer

Responsibilities : - Develop a strong intuitive understanding of various machine learning algorithms, including both supervised and unsupervised modeling techniques. - Demonstrate experience working with large, real-world datasets characterized by their size, complexity, incompleteness, and presence of errors. - Utilize your experience with various machine learning tools and libraries (e.g., scikit-learn, TensorFlow, PyTorch, etc.) to build and deploy models. - Apply machine learning techniques specifically to time-series data for forecasting, anomaly detection, and other relevant tasks. - Develop a strong intuition about algorithm and system performance, enabling you to optimize models for efficiency and scalability. - (Pluses) Leverage experience in deep learning, computer vision, topic modeling, or graph algorithms to tackle advanced problems. - Effectively communicate complex technical concepts and findings through strong interpersonal, written, and verbal communication Required : - Intuitive understanding of machine learning algorithms (supervised, unsupervised). - Experienced working with large, real-world, and often messy datasets. - Hands-on experience with machine learning tools and libraries (e.g., scikit-learn, TensorFlow, PyTorch). - Proven experience applying ML techniques to time-series data. - Strong intuition about algorithm and system performance. - Strong interpersonal, written, and verbal communication to Have : - Experience with deep learning frameworks. - Background in computer vision. - Experience with topic modeling techniques. - Knowledge of graph algorithms and their applications. - Experience with cloud platforms (AWS, Azure, GCP) for ML deployments. - Familiarity with data engineering pipelines and tools (ref:hirist.tech)

Location: bangalore, IN

Posted Date: 5/3/2025