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Jobs or company...

Data Scientist with LLM exp @ Onsite (Only w2)

Salary undisclosed
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Data Scientist with LLM exp

Location: Charlotte, NC

Duration: 12 Months+

This role is restricted to W2 candidates, with no sponsorship offered.

Key Responsibilities:

  • Optimize and deploy AI models on GPU clusters for enhanced performance and scalability.
  • Leverage multi-GPU training and distributed computing frameworks such as TensorFlow Distributed, PyTorch Distributed, and Horovod to accelerate AI/ML workloads.
  • Configure and manage NVIDIA GPU and Google Cloud Platform (GCP) resources, including TPUs and GPU instances.
  • Develop APIs and design cloud-native architectures to support AI/ML applications.
  • Work with Generative AI frameworks such as LLaMA and Mistral to drive innovation in AI solutions.
  • Utilize FastAPI, Unicorn, and Swagger to create efficient and scalable API solutions.
  • Implement solutions using Python, Apache Spark (PySpark), Kubernetes, and Django to ensure robust and scalable applications.
  • Employ Apache Kafka for real-time data streaming and ensure seamless integration with distributed computing frameworks.

Must-Have Qualifications:

  • Proven experience in optimizing and deploying AI models on GPU clusters.
  • Strong expertise in multi-GPU training and distributed computing frameworks, including TensorFlow Distributed, PyTorch Distributed, and Horovod.
  • Proficiency in configuring and managing NVIDIA GPU resources and GCP instances.
  • Knowledge of API development and cloud-native architectures.
  • Experience with Generative AI frameworks such as LLaMA and Mistral.
  • Proficient in FastAPI, Unicorn, and Swagger for API development.
  • Skilled in Python, Apache Spark (PySpark), Kubernetes, and Django.
  • Experience with Apache Kafka for real-time data streaming and integration with distributed systems.

Data Scientist with LLM exp

Location: Charlotte, NC

Duration: 12 Months+

This role is restricted to W2 candidates, with no sponsorship offered.

Key Responsibilities:

  • Optimize and deploy AI models on GPU clusters for enhanced performance and scalability.
  • Leverage multi-GPU training and distributed computing frameworks such as TensorFlow Distributed, PyTorch Distributed, and Horovod to accelerate AI/ML workloads.
  • Configure and manage NVIDIA GPU and Google Cloud Platform (GCP) resources, including TPUs and GPU instances.
  • Develop APIs and design cloud-native architectures to support AI/ML applications.
  • Work with Generative AI frameworks such as LLaMA and Mistral to drive innovation in AI solutions.
  • Utilize FastAPI, Unicorn, and Swagger to create efficient and scalable API solutions.
  • Implement solutions using Python, Apache Spark (PySpark), Kubernetes, and Django to ensure robust and scalable applications.
  • Employ Apache Kafka for real-time data streaming and ensure seamless integration with distributed computing frameworks.

Must-Have Qualifications:

  • Proven experience in optimizing and deploying AI models on GPU clusters.
  • Strong expertise in multi-GPU training and distributed computing frameworks, including TensorFlow Distributed, PyTorch Distributed, and Horovod.
  • Proficiency in configuring and managing NVIDIA GPU resources and GCP instances.
  • Knowledge of API development and cloud-native architectures.
  • Experience with Generative AI frameworks such as LLaMA and Mistral.
  • Proficient in FastAPI, Unicorn, and Swagger for API development.
  • Skilled in Python, Apache Spark (PySpark), Kubernetes, and Django.
  • Experience with Apache Kafka for real-time data streaming and integration with distributed systems.