ML Engineer + Data Scientist

AI Engineer with more focus on ML + ML OPS + Python

Machine Learning Engineering:

• Design, develop, train, and deploy Machine Learning and Deep Learning models for business use cases.
• Build scalable ML pipelines for data ingestion, feature engineering, model training, and inference.
• Optimize model performance, accuracy, latency, and scalability.
• Implement MLOps practices including CI/CD, model monitoring, versioning, and automated retraining.
• Deploy ML models in cloud environments using containerization and orchestration technologies.
• Collaborate with software engineering teams to integrate AI/ML solutions into production applications.

Data Science:

• Analyze large structured and unstructured datasets to identify trends, patterns, and actionable insights.
• Perform exploratory data analysis (EDA) and statistical modeling.
• Develop predictive, classification, recommendation, forecasting, and anomaly detection models.
• Conduct A/B testing and hypothesis validation to support business decisions.
• Create data visualizations, dashboards, and executive-level insights.
• Work closely with business stakeholders to translate business problems into AI/ML solutions.