Deep Learning Engineer

💼 Level: Mid-Level

We’re looking for a highly skilled and innovative Deep Learning Engineer to join our technology and AI team and design, develop, and deploy advanced deep learning solutions that solve complex business and real-world challenges. In this role, you will work across neural networks, computer vision, natural language processing, generative AI, and advanced machine learning to build scalable and high-performing intelligent systems. You'll collaborate closely with AI scientists, machine learning engineers, data scientists, software developers, data engineers, product managers, and cloud specialists to transform research and data into reliable production-ready AI solutions. If you're passionate about deep learning, artificial intelligence, and building next-generation intelligent technologies, we'd love to hear from you!

🎯 Key Responsibilities

• Design, develop, train, evaluate, and deploy deep learning models for real-world applications.
• Develop neural network architectures and deep learning solutions for complex business and technical challenges.
• Build solutions using technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and other modern deep learning architectures.
• Develop and optimize models for applications including computer vision, natural language processing, speech recognition, recommendation systems, forecasting, and generative AI.
• Prepare, clean, transform, and manage large-scale structured and unstructured datasets for deep learning applications.
• Perform feature engineering, data augmentation, model training, fine-tuning, validation, and performance optimization.
• Develop and implement deep learning solutions using frameworks such as PyTorch, TensorFlow, Keras, or similar technologies.
• Collaborate with AI researchers, data scientists, and machine learning engineers to transition experimental models into scalable production systems.
• Optimize deep learning models for accuracy, inference speed, memory efficiency, scalability, and computational performance.
• Work with GPUs, distributed computing, and cloud-based infrastructure to support large-scale model training and deployment.
• Develop model evaluation, testing, monitoring, and continuous improvement processes to maintain reliable AI performance.
• Integrate deep learning models into APIs, software applications, cloud platforms, edge devices, and intelligent products.
• Troubleshoot model performance, data quality, training, deployment, and production issues.
• Research emerging deep learning architectures, foundation models, multimodal AI, generative AI, and industry developments.
• Promote responsible AI practices covering data privacy, security, fairness, explainability, transparency, and model governance.
• Maintain technical documentation covering model architectures, experiments, datasets, evaluation results, and deployment processes.
• Contribute to continuous improvement initiatives that enhance AI capabilities, model performance, engineering efficiency, and business outcomes.

✅ Requirements

• Strong experience in deep learning, machine learning, artificial intelligence, computer science, or a related technical field.
• Strong proficiency in Python and experience developing production-quality AI and machine learning applications.
• Solid understanding of neural networks, deep learning algorithms, optimization techniques, model training, and evaluation.
• Practical experience with deep learning frameworks such as PyTorch, TensorFlow, Keras, or similar technologies.
• Experience working with architectures such as CNNs, RNNs, transformers, or other modern neural network models.
• Knowledge of computer vision, natural language processing, speech processing, generative AI, or multimodal AI is advantageous.
• Strong understanding of data preprocessing, feature engineering, model validation, hyperparameter tuning, and performance optimization.
• Experience working with large-scale datasets, distributed training, GPU computing, or high-performance computing environments is advantageous.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, Google Cloud, or similar technologies.
• Understanding of MLOps, model deployment, monitoring, Docker, Kubernetes, CI/CD, and production AI workflows is advantageous.
• Experience with large language models, foundation models, model fine-tuning, transfer learning, or reinforcement learning is advantageous.
• Strong analytical thinking, troubleshooting, debugging, and problem-solving skills.
• Excellent communication and collaboration abilities with technical and non-technical stakeholders.
• Ability to translate complex technical requirements into practical, scalable, and reliable deep learning solutions.
• High level of professionalism, curiosity, adaptability, accountability, and commitment to continuous learning.
• Bachelor's or master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Engineering, or a related field is preferred.
• PhD or research experience in deep learning, artificial intelligence, computer vision, natural language processing, or a related discipline is advantageous.

🌟 What We Offer

• An exciting opportunity to develop advanced deep learning and artificial intelligence solutions.
• Exposure to cutting-edge technologies including generative AI, large language models, transformers, computer vision, natural language processing, multimodal AI, and intelligent automation.
• Opportunities to work on challenging real-world AI applications with meaningful business and customer impact.
• Career growth and professional development opportunities in a rapidly evolving technology field.
• Ongoing technical training, mentorship, research opportunities, and certification support.
• Opportunities to collaborate with experienced AI scientists, machine learning engineers, data scientists, software developers, and technology leaders.
• Access to modern AI frameworks, cloud platforms, GPU infrastructure, advanced computing resources, and emerging deep learning technologies.
• Competitive salary and comprehensive benefits package.
• A collaborative and research-driven technology culture that values innovation, responsible AI, technical excellence, and continuous learning.
• The opportunity to make a meaningful impact by developing intelligent systems that improve automation, enhance decision-making, solve complex problems, and create long-term business value.