Job Title: Deep Learning Engineer
Job Summary:
We are seeking an Expert Deep Learning Engineer to lead the research, development, optimization, and deployment of cutting-edge AI models for enterprise applications. The ideal candidate should have deep expertise in neural networks, large-scale distributed training, self-supervised learning, reinforcement learning, and cloud-based AI solutions. This role involves mentoring ML teams, leading AI strategy, designing scalable architectures, and integrating AI advancements into real-world applications.
Key Responsibilities:
- Architect and optimize large-scale deep learning models for computer vision, NLP, speech recognition, and reinforcement learning
- Lead deep learning research, working with self-supervised learning, meta-learning, multimodal AI, and generative models
- Develop highly optimized neural networks, including CNNs, RNNs, Transformers, GANs, VAEs, and Diffusion Models
- Implement and optimize state-of-the-art AI architectures (GPT, T5, BERT, CLIP, Whisper, ViTs, Stable Diffusion, DALL·E)
- Leverage distributed computing and federated learning for large-scale model training (Horovod, Ray, Spark ML)
- Deploy AI models at scale using cloud infrastructure, ensuring low-latency, high-performance inference (AWS SageMaker, Google Vertex AI, Azure ML, NVIDIA Triton)
- Develop real-time AI applications for autonomous systems, edge AI, and robotics
- Implement MLOps best practices, including CI/CD, model monitoring, drift detection, and automated retraining
- Optimize models for inference on GPUs, TPUs, and specialized AI accelerators (NVIDIA CUDA, TensorRT, ONNX)
- Ensure AI security, privacy, and compliance, handling adversarial attacks, model robustness, and explainability
- Collaborate with research teams, industry experts, and business stakeholders to drive AI innovation and adoption
- Mentor and train ML engineers and data scientists, fostering a culture of AI excellence
- Stay ahead of AI advancements, contributing to research publications, patents, and conferences
Skills and Knowledge Required:
- Expert-level Python proficiency (Julia, R, Scala optional)
- Mastery of deep learning frameworks (TensorFlow, PyTorch, JAX, Keras)
- Extensive experience with large-scale model training, optimization, and deployment
- Deep understanding of advanced AI architectures, including:
- Transformers (GPT, BERT, T5, ViT, DINO, CLIP, Whisper)
- GANs, VAEs, Stable Diffusion, DALL·E
- Reinforcement Learning (DQN, PPO, SAC, A3C, AlphaFold, AlphaZero)
- Self-Supervised Learning & Contrastive Learning
- Neural Architecture Search (NAS) and AutoML
- Experience in distributed ML and parallel computing (Horovod, Ray, Spark ML, TPUs)
- Proficiency in cloud-based AI platforms (AWS SageMaker, Google Vertex AI, Azure ML, Snowflake ML)
- Hands-on experience with model compression (quantization, pruning, knowledge distillation)
- Experience with multi-agent reinforcement learning, federated learning, and edge AI
- Familiarity with quantum machine learning (QML) and neuromorphic computing (optional but a plus)
- Expertise in explainable AI (XAI), fairness, bias detection, and AI ethics frameworks
- Ability to deploy AI models at scale using Kubernetes, Docker, TensorFlow Serving, ONNX
Educational Qualifications:
- PhD or Master’s in Computer Science, AI, Data Science, Mathematics, or a related field
- Published research, patents, or contributions to AI conferences (NeurIPS, ICML, CVPR, ICLR) are a plus
- Certifications in Deep Learning, Cloud AI, or MLOps (AWS, GCP, Coursera, Udacity, MIT AI, NVIDIA AI) preferred
Experience:
- 10+ years of hands-on experience in deep learning, AI research, and scalable model deployment
- Proven experience in handling multi-terabyte datasets and large-scale AI projects
- Experience leading AI research and development teams, contributing to real-world AI innovations
Key Focus Areas:
- Next-Generation AI Research & Innovation
- Large-Scale Deep Learning & Self-Supervised Learning
- AI Model Optimization & Cloud-Based Deployment
- MLOps & Automated Model Management
- AI Security, Fairness, and Explainability
Tools and Technologies:
- Programming Languages: Python (Julia, R, Scala optional)
- Deep Learning Frameworks: TensorFlow, PyTorch, JAX, Keras
- Big Data & Distributed ML: Apache Spark, Dask, Ray, Horovod
- Cloud Platforms: AWS SageMaker, Google Vertex AI, Azure ML, Snowflake ML
- Deployment & MLOps: Kubernetes, MLflow, Airflow, FastAPI, TensorFlow Serving, NVIDIA Triton
- AI Model Optimization: TensorRT, ONNX, Pruning, Quantization, AutoML
- Data Engineering & Storage: SQL, NoSQL, Delta Lake, Snowflake, BigQuery
- Version Control & CI/CD: Git, DVC, Jenkins, Terraform, Kubeflow
Other Requirements:
- Recognized leader in AI research and real-world AI deployments
- Ability to drive AI innovation and bridge research with practical applications
- Excellent problem-solving skills for complex AI challenges
- Strong communication and leadership skills to influence AI adoption
- Passion for AI ethics, fairness, and responsible AI development