Machine Learning Engineer

Job Title : Machine Learning Engineer

Location : [Remote/Chennai]

Job Type : Full-Time

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.
  
  

• 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.
  
  

• 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.
  
  

• 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.
  
  

• Masters 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.