Senior Deep Learning Engineer

Design, develop, train, and deploy advanced deep learning and AI models for production applications. Lead the development of machine learning solutions from problem definition and data preparation through model deployment and monitoring. Research and implement state-of-the-art techniques in deep learning, generative AI, computer vision, NLP, or speech AI , depending on business requirements. Develop and optimize neural network architectures using frameworks such as PyTorch, TensorFlow, or JAX . Prepare, preprocess, and analyze large-scale datasets for model training and evaluation. Design training pipelines, evaluation methodologies, and experiments to improve model accuracy, robustness, and generalization. Fine-tune and optimize foundation models, including transformer-based and other large-scale models, where applicable. Optimize models for latency, memory usage, throughput, scalability, and inference cost . Develop distributed training and GPU-accelerated workflows for large models and datasets. Deploy models into production environments using appropriate MLOps, cloud, containerization, and CI/CD practices. Monitor production models, investigate performance issues, and implement continuous improvements. Establish model evaluation, testing, versioning, reproducibility, and experiment-tracking practices. Mentor junior and mid-level engineers and provide technical guidance on deep learning projects. Review code, model architectures, experiments, and technical designs to maintain engineering quality. Collaborate with software engineers, data scientists, product managers, and domain experts. Translate business requirements into scalable and measurable AI solutions. Stay current with advances in deep learning, foundation models, generative AI, and machine learning research and assess their practical applicability.