Primary Responsibilities

• Design and implement machine learning models to solve business problems using supervised, unsupervised, and deep learning techniques.
• Collaborate with data scientists to refine models and optimize performance.
• Build scalable data pipelines and ML infrastructure for model training, validation, and deployment.
• Deploy and monitor models in production environments using MLOps best practices.
• Analyze large datasets to extract meaningful patterns and features.
• Work with cross-functional teams to understand requirements and deliver AI-driven solutions.
• Conduct research and stay updated on the latest ML techniques, tools, and technologies.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
• Strong programming skills in Python (preferred), with experience in ML libraries such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.
• Proficiency in data processing frameworks like Pandas, NumPy, and experience with SQL and big data tools (e.g., Spark, Hadoop) is a plus.
• Solid understanding of machine learning algorithms, model evaluation, feature engineering, and data preprocessing.
• Experience with deploying ML models using Docker, Kubernetes, or cloud platforms (AWS/GCP/Azure).
• Familiarity with version control (Git) and CI/CD pipelines for ML workflows.

Preferred Qualifications

• Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker.
• Familiarity with data visualization tools like Matplotlib, Seaborn, or Plotly.
• Knowledge of model interpretability techniques (e.g., SHAP, LIME).
• Background in statistical analysis and A/B testing.

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