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 have

  strong proficiency in Python and experience with core ML libraries such as scikit-learn, XGBoost, LightGBM, and CatBoost

• Must have

  hands-on experience with deep learning frameworks including TensorFlow, PyTorch

• Must have

  solid understanding of machine learning algorithms, statistical methods, and model evaluation techniques

• Must have

  experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies Docker or Kubernetes

• Need to have

  proficiency in SQL and experience working with both structured and unstructured datasets

• Need to have

  knowledge of big data technologies such as Spark, Hadoop, or distributed computing frameworks

• Must be familiar with

  MLOps tools and platforms such as MLflow, Kubeflow, or similar model lifecycle management systems

• Must have

  experience with version control systems (Git) and CI/CD pipelines for ML workflows

• Need to have

  knowledge of data visualization tools and libraries such as Matplotlib, Seaborn, Plotly, or Tableau

• Must be familiar with

  software engineering best practices, including code testing, debugging, and performance optimization

• Need to have

  understanding of software development methodologies such as Agile or Scrum

• Possess

  excellent analytical and problem-solving skills with ability to work independently and in team environments

Additional Information:

• Must have

  Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field

• Preferred

  3+ years of experience in machine learning engineering or related roles

• Preferred

  experience with real-time inference systems and model serving architectures

Preferred