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-scale ML models.
Hands-on experien

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