Design, develop, and deploy machine learning models using Python, PySpark, Databricks ML, and Delta Lake
Partner with business and technical stakeholders to identify and deliver impactful AI/ML use cases
Build automated ML pipelines for model training, validation, and deployment
Manage feature stores and continuously monitor model performance and reliability using MLflow
Apply machine learning techniques to solve real business challenges and extract meaningful insights from data
Optimize models and pipelines for scalability, performance, and cost efficiency
Test, validate, and iterate on models to ensure accuracy and robustness
Stay current with emerging AI/ML technologies and help advance NEPs AI strategy

Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or a related field
3+ years in AI/ML, data science, or data engineering
Strong hands-on experience with Python, PySpark, distributed computing, and Databricks / Delta Lake
Proven ability to design, train, and deploy production-grade machine learning models
Solid foundation in statistics, mathematics, and machine learning algorithms
Experience working in cloud environments (AWS, Azure, or GCP)
Familiarity with MLOps best practices, including ML pipelines, model versioning, CI/CD, and performance monitoring
Experience with governed data environments, data quality/drift monitoring, and advanced ML techniques (e.g., NLP, entity resolution, AI agents)

AI/Machine Learning Engineer

Key skills