Data Scientist

2 weeks ago

Jaipur, Rajasthan, India Durapid Technologies Pvt Ltd Full time
Key Responsibilities:
  • Cloud Solutions Design & Implementation: Develop and maintain robust, scalable, and secure cloud solutions on platforms such as AWS and Azure.
  • MLOps & Automation: Build and optimize MLOps pipelines on cloud platforms, ensuring efficient deployment, monitoring, and scaling of machine learning models.
  • CI/CD Pipeline Orchestration: Implement CI/CD pipelines using tools like GitLab CI, GitHub Actions, and Jenkins to automate and streamline workflows across data science projects.
  • Data Pipeline & Engineering Infrastructure: Design, develop, and manage data pipelines and infrastructure to support large-scale enterprise machine learning systems, ensuring high availability and low latency processing.
  • Infrastructure Management: Oversee network operations and infrastructure management for machine learning and AI systems, ensuring optimized performance, security, and reliability.
  • Collaboration with DevOps: Collaborate with DevOps teams to manage containerization (Docker, Kubernetes) and infrastructure as code (Terraform, CloudFormation) for ML model deployment and monitoring.
  • Deep Learning & NLP: Apply cutting-edge Deep Learning and Natural Language Processing techniques to solve complex problems, particularly in areas like text analysis, speech recognition, and language generation.
  • Large Language Model (LLM) Infrastructure: Design and maintain infrastructure supporting Large Language Models (LLMs), enabling scalable deployment, optimization, and real-time inference.
  • Continuous Learning & Development: Stay updated with the latest advancements in data science, cloud infrastructure, and MLOps to drive innovation within the organization.
Skills & Qualifications:
  • 34 years of experience in Data Science, Machine Learning, or related fields with expertise in cloud infrastructure and MLOps.
  • Hands-on experience with cloud platforms such as AWS, Azure, or GCP, with strong knowledge of cloud-native services for machine learning and AI.
  • Expertise in MLOps tools like MLflow, Kubeflow, or SageMaker.
  • Strong proficiency in CI/CD pipelines using tools such as GitLab CI, GitHub Actions, or Jenkins.
  • Deep knowledge of DevOps practices and tools, including Docker, Kubernetes, Terraform, and Ansible.
  • Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
  • Hands-on experience with Natural Language Processing (NLP) libraries and techniques.
  • Strong programming skills in Python, R, or similar languages.
  • Proven ability to design, deploy, and manage large-scale LLM infrastructure.
  • Understanding of network operations and infrastructure management for cloud environments.
  • Strong analytical and problem-solving skills with a focus on delivering scalable solutions.

Key Technologies: machine learning, data science, cloud, pipelines, infrastructure

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