Senior Large Language Model Architect

2 days ago

Mumbai, Maharashtra, India beBeeMachine Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

We are looking for an experienced AI Architect to lead the development of our intelligent solutions. This role requires a strong understanding of machine learning principles and algorithms, with a focus on large language models (LLMs) such as GPT-4 BERT and similar architectures.

Key Responsibilities:
  • Develop and apply deep knowledge of computer architecture, especially GPUs, to maximize utilization and efficiency.
  • Work with cloud platforms (AWS, Azure, GCP) to manage and optimize resources for training large-scale deep learning models.
  • Utilize containerization and orchestration tools (Docker, Kubernetes) for scalable and reproducible ML deployments.
  • Apply principles of parallel and distributed computing, including distributed training for deep learning models.
  • Work with big data and distributed computing technologies (Hadoop, Spark) to handle large-volume datasets.
  • Implement MLOps practices and use related tools to manage the complete ML lifecycle.
  • Contribute to the infrastructure side of multiple ML projects, particularly those involving deep learning models such as BERT and Transformers.
  • Manage resources and optimize performance for large-scale ML workloads, both on-premise and in the cloud.
  • Handle challenges in training large models, including memory management, optimizing data loading, and troubleshooting hardware issues.
Requirements:
  • Strong knowledge of machine learning and deep learning algorithms, especially LLMs.
  • Proficiency in Python and deep learning frameworks (TensorFlow, PyTorch, Keras).
  • Expertise in GPU architecture and optimization.
  • Experience with parallel and distributed computing concepts.
  • Hands-on with containerization (Docker) and orchestration (Kubernetes).
Tech Stack and Tools:
  • Cloud: AWS, Azure, GCP.
  • Big Data: Hadoop, Spark.
  • MLOps Tools: MLflow, Kubeflow, or similar.
  • Infrastructure Optimization: Resource allocation, distributed training, GPU performance tuning.
Nice-to-Have:
  • Prior experience training large-scale deep learning models (BERT, Transformers).
  • Exposure to high-scale environments and large datasets.
  • Ability to troubleshoot hardware bottlenecks and optimize data pipelines.

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