Job Description
<p><p><b>Job Title :</b> Machine Learning Engineer<br/><br/><b>Job Type :</b> Full-Time<br/><br/><b>Experience Level :</b> Mid to Senior [5+ Years]<br/><br/><b>Department :</b> Data Science / AI Engineering<br/><br/><b>Job Summary :</b><br/><br/>We are seeking a highly skilled and mathematically grounded Machine Learning Engineer to join our AI team.<br/><br/> The ideal candidate will have 5+ years of ML experience with a deep understanding of machine learning algorithms, statistical modeling, and optimization techniques, along with hands-on experience in building scalable ML systems using modern frameworks and tools.<br/><br/><b>Key Responsibilities :</b><br/><br/>- Design, develop, and deploy machine learning models for real-world applications.<br/><br/>- Collaborate with data scientists, software engineers, and product teams to integrate ML solutions into production systems.<br/><br/>- Understand the mathematics behind machine learning algorithms to effectively implement and optimize them.<br/><br/>- Conduct mathematical analysis of algorithms to ensure robustness, efficiency, and scalability.<br/><br/>- Optimize model performance through hyperparameter tuning, feature engineering, and algorithmic improvements.<br/><br/>- Stay updated with the latest research in machine learning and apply relevant findings to ongoing projects.<br/><br/><b>Required & Theoretical Foundations :</b><br/><br/>- Strong foundation in Linear Algebra (e.g., matrix operations, eigenvalues, SVD).<br/><br/>- Proficiency in Probability and Statistics (e.g., Bayesian inference, hypothesis testing, distributions).<br/><br/>- Solid understanding of Calculus (e.g., gradients, partial derivatives, optimization).<br/><br/>- Knowledge of Numerical Methods and Convex Optimization.<br/><br/>- Familiarity with Information Theory, Graph Theory, or Statistical Learning Theory is a plus.<br/><br/><b>Programming & Software Skills :</b><br/><br/>- Proficient in Python (preferred), with experience in libraries such as : NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn<br/><br/>- Experience with deep learning frameworks : TensorFlow, PyTorch, Keras, or JAX<br/><br/>- Familiarity with ML Ops tools : MLflow, Kubeflow, Airflow, Docker, Kubernetes<br/><br/>- Experience with cloud platforms (AWS, GCP, Azure) for model deployment.<br/><br/><b>Machine Learning Expertise :</b><br/><br/>- Hands-on experience with supervised, unsupervised, and reinforcement learning.<br/><br/>- Understanding of model evaluation metrics and validation techniques.<br/><br/>- Experience with large-scale data processing (e.g., Spark, Dask) is a plus.<br/><br/><b>Preferred Qualifications :</b><br/><br/>- Master's or Ph.D. in Computer Science, Mathematics, Statistics, or a related field.<br/><br/>- Publications or contributions to open-source ML projects.<br/><br/>- Experience with LLMs, transformers, or generative models</p><br/></p> (ref:hirist.tech)