- 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.).
Datagrid Solutions - Anywhere in India/Multiple Locations - iimjobs
Description
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:
NetOps:
Cloud Computing:
MLOps and DevOps:
Data Pipelines and Engineering Infrastructure:
Technology Evaluation and Integration:
Proof-of-Concept Development:
Requirements:
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.