Deep Learning

Job Title: Deep Learning Engineer

Job Summary:

We are looking for a Senior Deep Learning Engineer to lead the development, optimization, and deployment of deep learning models for computer vision, NLP, speech processing, and reinforcement learning applications. The ideal candidate will have strong expertise in neural network architectures, distributed training, model optimization, and MLOps best practices. This role involves mentoring junior engineers, driving AI strategy, and collaborating with research and engineering teams to build cutting-edge AI solutions.

Key Responsibilities:

• Lead the design and deployment of deep learning models for real-world applications in computer vision, NLP, time series forecasting, and autonomous systems
• Architect, train, and optimize advanced neural network models, including CNNs, RNNs, LSTMs, GANs, VAEs, and Transformers (BERT, GPT, Vision Transformers)
• Develop large-scale deep learning pipelines using TensorFlow, PyTorch, JAX, and Keras
• Implement and optimize transfer learning techniques (ResNet, EfficientNet, YOLO, CLIP, DINO, Whisper)
• Optimize deep learning models for speed, efficiency, and accuracy using pruning, quantization, knowledge distillation, and NAS (Neural Architecture Search)
• Deploy and manage AI models at scale using Docker, Kubernetes, TensorFlow Serving, ONNX, and NVIDIA Triton Inference Server
• Leverage GPU acceleration and distributed computing (NVIDIA CUDA, TensorRT, Ray, Horovod) for large-scale model training
• Work with cloud-based ML platforms (AWS SageMaker, Google Vertex AI, Azure ML, Snowflake) for scalable AI solutions
• Implement real-time AI applications and Edge AI solutions for IoT and embedded AI models
• Automate model lifecycle management using CI/CD, MLflow, Kubeflow, Airflow, and feature stores
• Collaborate with research teams to integrate cutting-edge AI research and self-supervised learning methods
• Ensure AI fairness, explainability, and compliance with SHAP, LIME, and AI governance frameworks
• Mentor and train junior AI engineers, enforcing deep learning best practices

Skills and Knowledge Required:

• Expert-level Python proficiency (R, Scala, Julia optional)
• Extensive experience with deep learning frameworks (TensorFlow, PyTorch, JAX, Keras)
• Strong understanding of advanced AI architectures, including transformers (BERT, GPT, T5, DINO), GANs, and self-supervised learning
• Experience in distributed deep learning using Horovod, Ray, Spark ML, and Dask
• Proficiency in cloud-based AI platforms (AWS SageMaker, Google Vertex AI, Azure ML, Snowflake ML)
• Expertise in hyperparameter tuning and AutoML techniques (Optuna, HyperOpt, Bayesian Optimization)
• Strong knowledge of reinforcement learning frameworks (Stable-Baselines3, RLlib, OpenAI Gym)
• Hands-on experience in deploying AI models at scale using CI/CD pipelines (Jenkins, Terraform, MLflow, Kubeflow)
• Deep understanding of model interpretability, bias detection, and explainability techniques
• Experience integrating AI models into production applications using APIs, microservices, and edge AI solutions

Educational Qualifications:

• Bachelor’s, Master’s, or PhD in Computer Science, AI, Data Science, Mathematics, or related field
• Certifications in Deep Learning, Cloud AI, or MLOps (AWS, GCP, Coursera, Udacity, MIT AI, NVIDIA AI) are a plus

Experience:

• 5-8+ years of hands-on experience in deep learning model development, deployment, and optimization
• Experience in handling large-scale ML projects, working with massive datasets, and implementing scalable AI solutions
• Strong background in leading AI-driven teams and mentoring junior ML engineers

Key Focus Areas:

• Advanced Neural Networks & Model Optimization
• Large-Scale AI Deployment & MLOps
• Transfer Learning & Self-Supervised Learning
• Scalable Cloud AI Solutions
• AI Ethics, Fairness & Explainability

Tools and Technologies:

• Programming Languages: Python (R, Scala, Julia 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, Flask, TensorFlow Serving, NVIDIA Triton
• Data Engineering & Storage: SQL, NoSQL, Delta Lake, Snowflake, BigQuery
• Version Control & CI/CD: Git, DVC, Jenkins, Terraform, Kubeflow

Other Requirements:

• Proven leadership in AI strategy, deep learning research, and large-scale AI deployments
• Ability to translate research innovations into production-ready AI solutions
• Exceptional problem-solving skills for tackling real-world AI challenges
• Passion for AI ethics, fairness, and responsible AI