AI/ML Engineer

The AI/ML Engineer will design, develop, and validate machine learning models that support product features and internal analytics. Working closely with data engineers and product owners, the engineer translates business requirements into data pipelines, feature sets, and model prototypes. The role owns end-to-end model development for assigned use cases, including data preparation, algorithm selection, training, evaluation, and documentation. By monitoring model performance in production, the engineer helps identify drift and suggests improvements. This position requires independent execution of defined projects, clear communication of technical findings, and collaboration with crossfunctional teams to ensure models meet accuracy and reliability standards.

• Gather and clean structured and unstructured data sets required for model training, documenting data lineage and quality checks.
• Develop feature engineering pipelines using Python and pandas to create reproducible inputs for machine learning algorithms.
• Select, implement, and finetune supervised learning models (e.g., regression, classification) with scikit-learn or TensorFlow, adhering to project constraints.
• Conduct systematic model evaluation, generating performance reports and visualizations for stakeholder review.
• Write reusable code modules and unit tests, maintaining version control with Git and following team coding standards.
• Deploy trained models to a staging environment using Docker containers and validate inference latency and resource usage.
• Monitor deployed models for performance decay, collect feedback, and propose retraining or parameter adjustments.
• Collaborate with product managers to translate business objectives into measurable model success criteria.
• Document model architecture, training procedures, and operational guidelines in the team's knowledge base.