Datagrid Solutions

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, LLM infrastructure & Kubernetes.

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.

• Manage cloudbased infrastructure on AWS and Azure, focusing on scalability and efficiency.
• Utilize containerization technologies like Docker and Kubernetes for application deployment.

• 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.

• 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.

• Design and develop data pipelines and engineering infrastructure to support enterprise machine learning systems.
• Transform offline models created by data scientists into productionready systems.
• Build scalable tools and services for machine learning training and inference.

• Identify and evaluate new technologies to enhance the performance, maintainability, and reliability of machine learning systems.
• Develop custom integrations between cloudbased systems using APIs.

• Facilitate the development and deployment of proofofconcept machine learning systems.
• Emphasize auditability, versioning, and data security during development.

• Strong software engineering skills in complex, multilanguage systems.
• Proficiency in Python and comfort with Linux administration.
• Experience working with cloud computing and database systems.
• Expertise in building custom integrations between cloudbased systems using APIs.
• Experience with containerization (Docker) and Kubernetes in cloud computing environments.
• Familiarity with dataoriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.).
• Ability to translate business needs into technical requirements.
• Strong understanding of software testing, benchmarking, and continuous integration.
• Exposure to machine learning methodology and best practices.
• Exposure to deep learning approaches and modeling frameworks (PyTorch, TensorFlow, Keras, etc.).

If you are a dynamic engineer with a diverse skill set, from cloud computing to MLOps and beyond, and you are eager to contribute to innovative projects in a collaborative environment, we encourage you to apply for this challenging and multifaceted role.