Senior ML Engineer

What is the project, and why should you care?

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.

You will be an excellent fit for this position if you have:

• 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.

Here are some of the things you’ll be working on:

• 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.