We are looking for a Senior ML Engineer to design, build, and optimize machine learning models and pipelines powering production systems. The ideal candidate brings deep hands-on experience across the ML lifecycle, with particular strength in recommender systems, deep learning, MLOps practices, and cloud-based ML infrastructure on AWS.
Requirements
- 4+ years of hands-on experience in machine learning engineering
- Strong proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost, etc.).
- Solid experience with deep learning — architecture design, training, hyperparameter tuning, and deployment of neural network models.
- Proven experience designing and deploying recommender systems.
- Hands-on experience with AWS SageMaker and broader AWS ML ecosystem.
- Practical experience setting up data processing and ML workflows on AWS.
- Strong MLOps skills.
- Solid understanding of the full ML lifecycle.
- Hands-on experience with containerization and orchestration in production environments.
- Proficiency with SQL and experience working with both structured and unstructured data sources.
- Strong problem-solving skills with an emphasis on scalability and performance optimization.
Responsibilities:
- Design, train, and iterate on ML and deep learning models for recommendation, ranking, and personalization use cases.
- Architect and maintain end-to-end ML pipelines on AWS.
- Set up and optimize data processing and ML workflows using AWS services.
- Build and maintain MLOps infrastructure.
- Collaborate with data engineers to ensure data quality, build feature stores, and prepare datasets for model training and inference.
- Evaluate and benchmark model performance, run offline and online experiments, and drive continuous improvement of model accuracy and efficiency.
- Optimize model serving infrastructure for latency, throughput, and cost-effectiveness.
- Partner with product and business stakeholders to translate requirements into well-scoped ML solutions.
- Document model architecture, assumptions, performance characteristics, and known limitations.
- Stay current with advances in recommendation systems, deep learning, and cloud ML services, and propose improvements to existing approaches.