Cloud Engineer
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Develop Reusable Components:
- Objective: Design and implement cloud components with reusability in mind.
- KPI: Ensure that 50% of the developed components are created as reusable components, reducing redundancy and promoting efficiency in future projects.
Implement Infrastructure as Code (IaC):
- Objective: Emphasize Infrastructure as Code (IaC) practices for cloud resource provisioning and management.
- KPI: Achieve 100% IaC implementation for all cloud resources, utilizing AWS CDK and optionally Terraform, to enhance deployment consistency and scalability.
Test and Documentation Coverage:
- Objective: Prioritize testing and comprehensive documentation to ensure system reliability and knowledge transferability.
- KPI: Maintain a test and documentation coverage of at least 50% for all developed components, promoting robustness, ease of maintenance, and knowledge sharing within the team.
Areas of Responsibility
Cloud Governance and Security:
- Implement and manage cloud governance practices.
- Design and enforce network security measures within cloud environments.
- Ensure Infrastructure as Code practices using AWS CDK and Terraform.
Application Development and DevOps:
- Develop application components and implement DevOps practices.
- Collaborate with development teams for prototype development and web development initiatives.
Data Management:
- Build and maintain ETL pipelines for efficient data transformation.
- Create and manage data lakes and data warehouses in the cloud.
Machine Learning Collaboration:
- Collaborate with the data science team.
- Implement and optimize machine learning workflows, including AI services integration.
Qualifications
Skills
Programming Skills:
- Proficient in Python for scripting, automation, and data manipulation.
- Experience with JavaScript (JS) is a significant advantage.
- Familiarity with web development concepts is a plus.
Cloud Expertise:
- Strong understanding of AWS services, with a focus on cloud governance, network security.
- Experience with Infrastructure as Code (IaC) using AWS CDK.
- Experience with Terraform is an advantage.
- Experience with GCP is a plus.
Data Expertise:
- Knowledge of ETL processes and data transformation using tools like Pandas.
- Competency in building and managing data lakes and data warehouses in the cloud.
Machine Learning:
- Experience with machine learning workflows and integrating AI services via APIs.
- Familiarity with AI/ML frameworks, such as Langchain, Llamaindex.
- Exposure to Generative Search technologies like ChromaDB and Weaviate is a significant plus.
Knowledge
Cloud Architecture:
- In-depth knowledge of AWS services and architecture.
- Understanding of cloud-based security best practices.
Data Management:
- Familiarity with streaming and batch processing concepts.
Machine Learning Lifecycle:
- Knowledge of the end-to-end AI/ML workflow and lifecycle.
Emerging Technologies:
- Awareness of emerging technologies and frameworks in machine learning.
Experience
Cloud Implementation:
- Proven experience in implementing cloud solutions, with a primary focus on AWS and additional exposure to GCP.
Infrastructure as Code:
- Successful deployment of Infrastructure as Code using AWS CDK and/or Terraform.
Data Management:
- Previous involvement in building and maintaining ETL pipelines.
Machine Learning Implementation:
- Hands-on experience with machine learning frameworks and AI services.
Development Exposure:
- Exposure to web development and prototype development is a plus.
We offer
- Scientific or engineering challenges
- Work with disruptive deep-tech startups
- Work with rock stars (senior-level engineers, Ph.D.)
- Meaningful social and environmental projects
- Transparent, professional growth plan depending on your impact
- Remote work from any location
- Flexible working hours
- Unpaid unlimited personal time off
- Regular Team Building & company-wide team events