Job Requirements
About the Role
As a Senior Machine Learning Engineer within the Data & Analytics team, you will be responsible for managing and optimizing the training infrastructure for Large Language Models (LLMs). This role demands a deep understanding of GPU architecture, machine learning principles, and distributed computing. You will lead Gen AI initiatives in a cross-functional setup, ensuring efficient resource utilization and timely delivery of large-scale ML projects.
Key Responsibilities
Primary Responsibilities
• Lead Generative AI projects in a cross-functional team workplace.
• Apply advanced machine learning principles and algorithms, particularly for LLMs such as GPT-4, BERT, and Transformers.
• Utilize deep learning frameworks like TensorFlow, PyTorch, and Keras for model training.
• Maximize GPU utilization and efficiency through deep knowledge of computer architecture.
• Manage and optimize cloud-based resources (AWS, Azure, GCP) for deep learning model training.
• Implement containerization and orchestration using Docker and Kubernetes.




• Apply parallel and distributed computing principles for scalable model training.
• Integrate big data technologies like Hadoop and Spark into ML workflows.
• Adopt MLOps practices and tools to manage the end-to-end ML lifecycle.

Secondary Responsibilities
• Manage infrastructure for multiple ML projects, especially those involving deep learning models.
• Optimize performance and resource allocation for large-scale ML tasks.
• Handle GPU resource management both on-premises and in the cloud.
• Address challenges in training large models, including memory management, data loading optimization, and hardware troubleshooting.
• Collaborate closely with data scientists and ML engineers to understand infrastructure needs and deliver efficient solutions.

What We Are Looking For
Education
• Graduation in BSC or BCA or B.Tech.

Experience
• 4+ years of relevant experience in managing infrastructure for training large

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