Machine Learning Engineer

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