Junior TensorFlow and PyTorch Deep Learning Engineer

What role will you play in the teamYou will serve as an integral team member focused on conceptualizing, training, and deploying cutting-edge deep learning architectures. Your contributions will directly drive our automated computer vision and natural language processing initiatives, translating academic research concepts into scalable, production-ready machine learning models that power core platform features.What you will doYou will write clean, modular Python code to build neural network models, conduct extensive hyperparameter tuning experiments, manage large annotated training datasets, evaluate model generalization metrics, and collaborate with backend developers to integrate trained models via robust inference endpoints.Key responsibilityDevelop, train, and validate deep neural network architectures using TensorFlow, Keras, and PyTorch frameworks.Preprocess, clean, and augment large unstructured datasets including image, text, and numerical streams.Design custom loss functions, optimization algorithms, and evaluation metrics tailored to specific business domains.Optimize trained models through quantization and pruning techniques to ensure low-latency inference on cloud infrastructure.Partner with data engineers to establish seamless pipelines feeding training features directly into modeling environments.Required Qualification and SkillsMaster's or Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or Mathematics.Solid foundational knowledge of machine learning algorithms, deep learning principles, and linear algebra.Hands-on programming proficiency in Python alongside TensorFlow, PyTorch, and scikit-learn libraries.Familiarity with version control workflows (Git) and containerization technologies like Docker.Excellent mathematical reasoning capability and demonstrated aptitude for empirical experimentation and research.Benefits IncludedCompetitive base salary complemented by milestone-based project incentives and annual performance increments.Robust medical insurance plan including outpatient consultations and hospitalization benefits.Dedicated annual learning budget for acquiring advanced AI/ML certifications and online courses.Flexible work environment supporting core operational hours and wellness initiatives.Modern office infrastructure equipped with high-performance computing hardware and recreational zones.A Day in the LifeYou will start your morning analyzing loss curves from overnight deep learning training jobs executed on cloud GPUs. Afterward, you will refactor a PyTorch data loader module to handle memory leaks, participate in a design sync with product managers regarding model inference latency requirements, and spend your afternoon tuning hyperparameters for an upcoming text classification release.