Work Experience: 3+ years
Industry: IT Services
Remote Job Job Description: Data Scientist (MLOps)We are seeking a versatile and adaptable Data Scientist with expertise in a range of technology domains, including Network Operations, Infrastructure Management, Cloud Computing, MLOps, Deep Learning, NLP, DevOps, and LLM infrastructure. This role encompasses a wide range of responsibilities, including designing and implementing cloud solutions, building MLOps pipelines on cloud platforms (AWS, Azure), orchestrating CI/CD pipelines using tools like GitLab CI and GitHub Actions, and taking ownership of data pipeline and engineering infrastructure design to support enterprise machine learning systems at scale.
Responsibilities:Infra: Manage cloud-based infrastructure on AWS and Azure, focusing on scalability and efficiency. Utilize containerization technologies like Docker and Kubernetes for application deployment.NetOps: Monitor and maintain network infrastructure, ensuring optimal performance and security. Implement load-balancing solutions for efficient traffic distribution.Infrastructure and Systems Management: Design and implement cloud solutions, including the development of MLOps pipelines. Ensure proper provisioning, resource management, and cost optimization in a cloud environment.MLOps and DevOps: Orchestrate CI/CD pipelines using GitLab CI and GitHub Actions for streamlined software delivery. Collaborate with data scientists and engineers to operationalize and optimize data science models. Apply software engineering rigor, including CI/CD and automation, to machine learning projects.Data Pipelines and Engineering Infrastructure: Design and develop data pipelines and engineering infrastructure to support enterprise machine learning systems. Transform offline models created by data scientists into production-ready systems. Build scalable tools and services for machine learning training and inference.Technology Evaluation and Integration: Identify and evaluate new technologies to enhance the performance, maintainability, and reliability of machine learning systems. Develop custom integrations between cloud-based systems using APIs.Proof-of-Concept Development: Facilitate the development and deployment of proof-of-concept machine learning systems. Emphasize auditability, versioning, and data security during development.Requirements:Strong software engineering skills in complex, multi-language systems.Proficiency in Python and comfort with Linux administration.Experience working with cloud computing and database systems.Expertise in building custom integrations between cloud-based systems using APIs.Experience with containerization (Docker) and Kubernetes in cloud computing environments.Familiarity with data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
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