Job Title: Deep Learning Engineer
Job Summary:
We are looking for a Mid-Level Deep Learning Engineer to design, train, optimize, and deploy deep learning models for computer vision, natural language processing (NLP), speech recognition, and reinforcement learning applications. The ideal candidate should have strong experience in deep learning frameworks, cloud-based AI services, and scalable model deployment. This role involves collaborating with data scientists, engineers, and business teams to build high-performance AI solutions that drive innovation.
Key Responsibilities:
- Develop and optimize deep learning models for image processing, NLP, time series forecasting, and autonomous systems
- Design, train, and fine-tune neural network architectures (CNNs, RNNs, Transformers, GANs, LSTMs, Vision Transformers)
- Work with large datasets, handling data augmentation, feature extraction, and dimensionality reduction
- Implement and optimize transfer learning techniques (ResNet, BERT, GPT, EfficientNet, YOLO, CLIP)
- Deploy deep learning models at scale using Docker, Kubernetes, TensorFlow Serving, and ONNX
- Utilize GPU acceleration for training and inference (NVIDIA CUDA, TensorRT, PyTorch Lightning)
- Monitor and improve model performance using hyperparameter tuning, pruning, quantization, and ensembling
- Develop automated model retraining pipelines for real-time AI applications
- Work with cloud-based ML solutions (AWS SageMaker, Google Cloud AI, Azure ML)
- Collaborate with software engineers and DevOps teams to integrate AI models into production systems
- Stay updated with the latest deep learning research, exploring advancements in AI ethics, explainability, and multimodal learning
Skills and Knowledge Required:
- Proficiency in Python (R, Julia, or Scala is a plus)
- Expertise in deep learning frameworks (TensorFlow, PyTorch, Keras)
- Strong understanding of deep neural networks (DNNs), CNNs, RNNs, GANs, and attention mechanisms
- Experience in NLP, computer vision, reinforcement learning, and generative models
- Proficiency in cloud-based ML platforms (AWS SageMaker, Google Vertex AI, Azure ML)
- Experience in big data frameworks (Apache Spark, Dask, Ray) for distributed model training
- Knowledge of hyperparameter tuning techniques (Grid Search, Bayesian Optimization, Optuna)
- Familiarity with model compression techniques (quantization, pruning, knowledge distillation)
- Hands-on experience with MLOps practices (MLflow, Kubeflow, Airflow, TensorFlow Extended)
- Experience in deploying AI models in production using FastAPI, Flask, REST APIs, and cloud-based microservices
Educational Qualifications:
- Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Mathematics, or related fields
- Certifications in Deep Learning (Coursera, Udacity, Fast.ai, TensorFlow, PyTorch, NVIDIA AI) are a plus
Experience:
- 3-5 years of experience in deep learning, model development, and AI deployment
- Proven experience in handling large-scale ML projects and deploying models into production
Key Focus Areas:
- Deep Learning Model Development & Optimization
- Neural Networks for Computer Vision, NLP & Speech Recognition
- AI Model Deployment & MLOps Best Practices
- Cloud-Based & Edge AI Deployments
Tools and Technologies:
- Programming Languages: Python (R, Scala optional)
- Deep Learning Frameworks: TensorFlow, PyTorch, Keras
- Data Processing & Visualization: NumPy, Pandas, OpenCV, Matplotlib, Seaborn
- Cloud & GPU Computing: AWS SageMaker, Google Cloud AI, Azure ML, NVIDIA CUDA, TensorRT
- Deployment & APIs: Flask, FastAPI, TensorFlow Serving, ONNX, Docker, Kubernetes
- Big Data & Distributed ML: Apache Spark, Dask, Ray
- Version Control & MLOps: Git, MLflow, DVC, Kubeflow
Other Requirements:
- Ability to lead deep learning projects and optimize AI models for real-world applications
- Strong problem-solving and analytical skills for AI-driven solutions
- Passion for AI research, innovation, and continuous learning
- Excellent teamwork and communication skills