Machine Learning Engineer

1 week ago

Mumbai, India People Realm Recruitment Services Private Limited Full time
Designation: Machine Learning Engineer

Experience: 4 to 9 years

Location: Mumbai

Primary Roles & Responsibilities:

Machine Learning Principles and Algorithms: Demonstrate a solid understanding of machine learning principles and algorithms, particularly Large Language Models (LLMs) such as GPT-4, BERT, etc.Deep Learning Frameworks: Possess extensive knowledge of deep learning frameworks like TensorFlow, PyTorch, or Keras, specifically for training LLMs.Computer Architecture and GPU Utilization: Deep understanding of computer architecture, especially in relation to GPUs. Proficient in maximizing GPU utilization and efficiency.Cloud Platforms Management: Familiarity with cloud platforms (AWS, Azure, GCP) and experience in effectively managing and optimizing resources for training deep learning models.Containerization and Orchestration: Experience with containerization and orchestration tools like Docker and Kubernetes to streamline deployment and management of deep learning models.Parallel and Distributed Computing: Understanding of parallel and distributed computing principles, with potential experience in distributed training of deep learning models.Big Data Technologies: Familiarity with distributed computing and big data technologies such as Hadoop and Spark, which are often utilized in conjunction with deep learning tasks.Machine Learning Operations (MLOps): Understanding of MLOps principles and proficiency in tools for managing the machine learning lifecycle efficiently.Secondary Responsibilities:

Infrastructure Management: Experience in the infrastructure side of multiple machine learning projects, particularly involving deep learning models like BERT and Transformers.Resource Optimization: Proven track record in managing and optimizing resources for large-scale machine learning tasks, both on-premise and in the cloud.GPU Resource Management: Experience in effectively managing and optimizing GPU resources for machine learning tasks, addressing challenges such as memory usage and troubleshooting hardware issues.Collaboration with Data Scientists and Engineers: Ideally, worked closely with data scientists and machine learning engineers to understand their infrastructure needs and provide efficient solutions.Key Success Metrics:

Timely Deliverables: Ensure timely delivery of project milestones and tasks.Spot Training Infrastructure Fixes: Quickly identify and resolve training infrastructure issues.Technical Leadership: Lead the technical aspects of projects, providing expertise in machine learning infrastructure.Error-Free Deliverables: Ensure deliverables are accurate and free from errors, meeting quality standards.

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