[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL] | GitHub: [Your GitHub URL]

PROFESSIONAL SUMMARY
Deep Learning Specialist with [X] years building and deploying neural networks for [computer vision, NLP, etc.]. Expert in PyTorch and TensorFlow with proven ability to improve model performance and reduce training time. Strong track record of [specific achievements in deep learning].

TECHNICAL SKILLS
Deep Learning: CNNs, RNNs, LSTMs, Transformers, GANs, Autoencoders, Attention Mechanisms
Frameworks: PyTorch, TensorFlow, Keras, Hugging Face, FastAI
Computer Vision: Object Detection, Image Segmentation, Image Classification, OCR
NLP: Text Classification, Named Entity Recognition, Sentiment Analysis, Language Models
Model Optimization: Quantization, Pruning, Knowledge Distillation, ONNX, TensorRT
Cloud & Compute: AWS, GCP, CUDA, Multi-GPU Training, Distributed Training

PROFESSIONAL EXPERIENCE

Deep Learning Engineer / Specialist
[Company Name], [City, State]
[Start Date] – Present
• Developed [specific architecture] achieving [X%] accuracy on [task], outperforming previous best by [Y%]
• Reduced model training time from [X] hours to [Y] minutes through optimization techniques
• Deployed [number] deep learning models to production serving [X]M predictions daily
• Implemented transfer learning approach reducing data requirements by [X%]
• Optimized inference speed by [Y]x through quantization and pruning maintaining [Z%] accuracy
• Collaborated with research team to implement state-of-the-art architectures

Deep Learning Engineer
[Previous Company Name], [City, State]
[Start Date] – [End Date]
• Built image classification system with [X%] accuracy processing [Y]K images per second
• Developed custom loss functions improving model convergence by [X%]
• Created data augmentation pipeline increasing effective dataset size by [Y]x
• Implemented distributed training reducing training time by [Z%]

TECHNICAL PROJECTS

[Project Name]: [Specific DL Application]
• Architecture: [ResNet, BERT, GPT, YOLO, etc.] with [custom modifications]
• Dataset: [Size and description]
• Results: Achieved [metric] of [X%] with [Y] parameter model
• Deployment: [Production details and scale]

[Project Name]: [Another DL Project]
• Challenge: [Specific problem addressed]
• Approach: [Architecture and techniques used]
• Innovation: [Novel contribution or optimization]
• Impact: [Quantified improvement]

PUBLICATIONS & RESEARCH

• [Paper Title], [Conference/Journal], [Year] - [Brief description]
• [Paper Title], [Venue], [Year] - [Brief description]

EDUCATION
[Degree Name], [Relevant Field]
[University Name], [City, State]
Graduation: [Year]
Thesis/Capstone: [Title if relevant to DL]

CERTIFICATIONS
• [Deep Learning Specialization, Coursera], [Year]
• [TensorFlow Developer Certificate], [Year]

TECHNICAL EXPERTISE
• Neural Architecture: Design custom architectures for specific problems
• Model Training: Hyperparameter tuning, regularization, batch normalization
• Transfer Learning: Fine-tuning pre-trained models, domain adaptation
• MLOps: Model versioning, experiment tracking, deployment pipelines