Machine Learning Engineer

Machine Learning Engineer About Astreya: Astreya offers comprehensive IT support and managed services. These services include Data Center and Network Management, Digital Workplace Services (like Service Desk, Audio Visual, and IT Asset Management), as well as Next-Gen Digital Engineering services encompassing Software Engineering, Data Engineering, and cybersecurity solutions. Astreya's expertise lies in creating seamless interactions between people and technology to help organizations achieve operational excellence and growth. Responsibilities: Design, develop, and deploy machine learning models and algorithms to solve complex business problems and drive data-driven decision making Collaborate with cross-functional teams including data engineers, software engineers, and business stakeholders to understand requirements and translate business objectives into scalable ML solutions Build and maintain robust ML pipelines for data preprocessing, feature engineering, model training, validation, and deployment Optimize model performance through hyperparameter tuning, feature selection, and algorithmic improvements Implement MLOps best practices including model versioning, monitoring, and automated retraining workflows Ensure data quality, integrity, and security throughout the ML lifecycle Scale ML systems to handle large datasets and high-throughput inference requirements Conduct A/B testing and experimentation to validate model performance and business impact Monitor deployed models for drift, performance degradation, and bias detection Create comprehensive documentation including technical specifications, model cards, and deployment guides for stakeholders Professional & Technical Skills: Must havestrong proficiency in Python and experience with core ML libraries such as scikit-learn, XGBoost, LightGBM, and CatBoost Must havehands-on experience with deep learning frameworks including TensorFlow, PyTorch Must havesolid understanding of machine learning algorithms, statistical methods, and model evaluation techniques Must haveexperience with cloud platforms (AWS, GCP, or Azure) and containerization technologies Docker or Kubernetes Need to haveproficiency in SQL and experience working with both structured and unstructured datasets Need to haveknowledge of big data technologies such as Spark, Hadoop, or distributed computing frameworks Must be familiar withMLOps tools and platforms such as MLflow, Kubeflow, or similar model lifecycle management systems Must haveexperience with version control systems (Git) and CI/CD pipelines for ML workflows Need to haveknowledge of data visualization tools and libraries such as Matplotlib, Seaborn, Plotly, or Tableau Must be familiar withsoftware engineering best practices, including code testing, debugging, and performance optimization Need to haveunderstanding of software development methodologies such as Agile or Scrum Possessexcellent analytical and problem-solving skills with ability to work independently and in team environments Additional Information: Must haveBachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field Preferred3+ years of experience in machine learning engineering or related roles Preferredexperience with real-time inference systems and model serving architectures Preferredknowledge of specialized domains such as computer vision, natural language processing, or recommendation systems

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