Datagrid Solutions

Job Description:

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.

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.

Cloud Computing:

- 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.).

- 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. Join our team and help us drive technological excellence across our organization.