Senior ML Engineer

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. Originally posted on Himalayas