AddressJob 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.
