The posting
Job Summary
The Senior AI/ML Engineer is responsible for building, integrating, and operationalizing the machine learning components of the Topic Ranking personalization solution. This role focuses on feature engineering, model enhancement, pipeline development, GenAI integration, deployment, and production-readiness across the training and serving lifecycle.
Key Responsibilities
- Develop and enhance the ranking model using the existing SharedBottomModel and multi-task learning framework.
- Engineer user-level behavioral features from ordering and interaction data.
- Integrate GenAI-generated user profile features and embeddings into model training and inference workflows.
- Build and adapt ETL pipelines for feature extraction, transformation, and profile generation.
- Implement projection layers, gating mechanisms, and preprocessing logic for embedding-based features.
- Execute model training, offline validation, and hyperparameter optimization workflows.
- Apply techniques such as inverse propensity scoring to address ranking bias.
- Deploy trained models to SageMaker endpoints and validate integration with the serving platform.
- Support shadow-mode validation, monitoring, model drift awareness, and operational readiness.
- Produce technical documentation and runbooks for deployment, retraining, and support handoff.
Required Skills
- Strong hands-on experience in machine learning engineering and production ML deployment.
- Proficiency in Python, PySpark, and ML pipeline development.
- Experience with Amazon SageMaker for training, tuning, batch processing, and endpoint deployment.
- Familiarity with feature engineering for recommendation or ranking systems.
- Strong understanding of embeddings, model integration patterns, and model-serving considerations.
- Experience working with distributed data pipelines, cloud storage, and production data environments.
- Ability to troubleshoot end-to-end ML workflows across data preparation, training, deployment, and monitoring.
Preferred Qualifications
- Experience integrating GenAI or LLM-generated outputs into downstream ML systems.
- Familiarity with feature stores, batch inference pipelines, and retraining orchestration.
- Experience supporting online experimentation or shadow deployments in production systems.
- Exposure to operational monitoring tools such as CloudWatch or model monitoring solutions.
- Prior experience in personalization, marketplace platforms, or consumer recommendation products.
