About the role

The role involves designing, developing, and deploying machine learning models that drive data-driven
decision-making across the organization’s products and platforms. The candidate will focus on building robust,
scalable ML solutions for prediction, classification, and optimization use cases. The role requires a
blend of technical expertise, analytical thinking, and business understanding to deliver measurable
impact.

Roles & Responsibilities

• Design, develop, train, test, and deploy end-to-end ML models for production-scale applications.R
• Work across the entire ML lifecycle – from data ingestion and feature engineering to model training, evaluation, and deployment.
• Build and maintain MLOps pipelines for scalable and reliable model deployment in real-time environments.
• Collaborate with cross-functional teams (engineering, product, and data) to translate business problems into ML solutions.
• Evaluate and optimize model performance, ensuring efficiency, scalability, and robustness.
• Stay updated on the latest trends in AI/ML, model monitoring, and automation frameworks.
• Experience in building real-time inference pipelines and production-grade ML systems.
• Proficiency in Python and common ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Familiarity with cloud platforms (AWS, Azure, or GCP) and CI/CD for ML.
• Excellent analytical and problem-solving skills with attention to scalability and performance.

Educational Qualification

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field from a Tier 1 institution.

• Minimum 5–8 years of hands-on experience in machine learning and data science.
• Strong expertise in Python, SQL, and machine learning libraries such as Scikit-learn,
  TensorFlow, and PyTorch.
• Experience with data processing tools (Pandas, NumPy, Spark) and visualization frameworks
  (Tableau, Power BI, or Matplotlib).
• Deep understanding of model evaluation metrics, statistical inference, and feature
  engineering.
• Hands-on experience with MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, CI/CD
  pipelines).
• Familiarity with cloud platforms such as AWS, Azure, or GCP for model deployment.
• Exposure to NLP, computer vision, or time-series models is an added advantage.