KPI

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.

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.

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.

• 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