Descripción de la oferta
This has the effect of a powerful, positive culture that helps create not only internal growth but projects our passion to our partners and the outside world.
Performance (V2) : Built with scalability in mind, making it easier to handle growing amounts of content and traffic, and deploy new experiences. At Spotlio we treat AI as a standard part of the engineering toolkit. We expect that AI tools are used daily to work faster and to higher quality — while keeping a human in the loop for anything that touches production, security, or customer data. We value employees who experiment with new AI capabilities, build reusable automations, and help the rest of the team level up.
This involves actively listening to customer feedback, understanding their needs and pain points, and continuously working to improve their experience with your product or service.
Innovation (V4) : Ambition is not just a buzzword for us: we aim to grow the right way. Ensure that systems are safe and secure against cybersecurity threats (V1, V2, V3, V4)
Identify technical problems and develop software updates and ‘fixes’ (V1, V2, V3, V4)
Work with software developers and software engineers to ensure that development follows established processes and works as intended (V1, V2, V3, V4)
Plan out projects and be involved in project management decisions (V1, V2, V3, V4)
Participate in the daily stand-up meetings, retrospectives and other meetings involved in the SCRUM process (V1, V2, V3, V4)
Use AI coding assistants and agentic tools (e.g. Claude / Claude Code, Copilot, Cursor) to accelerate authoring and review of IaC, deployment scripts, and automation — applying human judgment to validate output before it reaches production (V1, V2, V4)
Apply AI to operational signals — log, metric, and trace analysis, incident triage, and root-cause investigation — to detect failures and bottlenecks faster (V2, V3, V4)
Use AI tools responsibly and securely: never expose secrets, credentials, or customer data in prompts, and follow established data-handling and compliance practices when working with AI services (V2, V3, V4)
The Infrastructure Engineer communicates with software developers, data analysts, support, and delivery teams.
Willingness to work outside office hours with notice.
Experienced in managing and scaling Amazon Web Services (AWS):
RDS
Cloud Watch
Cloudformation, Terraform, Serverless or any other IaC systems
EC2
ELB and WAF
Redshift
CloudFront
S3
ECS and deployment of Docker containers
Lambda
Redis
Experienced in using server monitoring tools
Infrastructure as Code: Terraform, CloudFormation, etc
Experience with CD-CI Tools: CircleCI, Bitbucket Pipelines, etc..
Serverless Framework
Hands-on experience with at least one AI coding assistant or agentic developer tool (Claude Code, GitHub Copilot, Cursor, or similar) in a real engineering workflow
Sound judgment about AI limitations: reviews, tests, and verifies AI-generated code and configuration rather than accepting it blindly; Awareness of the security and privacy considerations of using AI services (data leakage, prompt injection, supply-chain risk)
Proficient in writing and having a conversation in English
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