In this role, you will design, build, and operate scalable platforms and infrastructure that enable engineering teams to develop, deploy, and run services efficiently, securely, and reliably.
You will work in a complex, large-scale production environment using AWS, Kubernetes, Infrastructure as Code, automation, observability, and AI-driven technologies.
What youll do:
Design, build, and maintain application infrastructure and platforms used by engineering teams.
Develop scalable, reliable, and secure infrastructure on AWS.
Build and improve CI/CD pipelines, deployment processes, and infrastructure automation.
Manage and operate Kubernetes-based production environments.
Develop self-service tools and platform capabilities that improve developer productivity and experience.
Improve system reliability, availability, performance, and scalability.
Define and implement best practices for infrastructure, deployments, observability, and security.
Lead troubleshooting, incident investigation, and root cause analysis for complex production issues.
Collaborate with R&D, Security, Architecture, Data, and Cloud teams.
Evaluate and implement AI-based tools for infrastructure automation, operational efficiency, incident analysis, and developer workflows.
Develop or integrate AI agents, LLM-based tools, and AIOps capabilities into platform engineering processes.
What you have:
3-5 years of hands-on experience in DevOps, Platform Engineering, or Infrastructure Engineering roles.
Strong hands-on experience working with AWS in production environments.
Proven experience with AWS services such as EKS, EC2, IAM, VPC, Route 53, S3, RDS, Lambda, CloudWatch, or similar services.
Strong experience with Kubernetes and containerized environments.
Hands-on experience with Infrastructure as Code tools, preferably Terraform.
Experience building and maintaining CI/CD pipelines using GitHub Actions, GitLab CI, Argo CD, or similar technologies.
Experience writing automation or production-grade code using Python, Go, Bash, or a similar language.
Strong understanding of Linux, networking, DNS, load balancing, and distributed systems.
Experience with monitoring, logging, tracing, and observability platforms.
Strong troubleshooting skills and the ability to investigate complex production issues.
Ability to work independently, take ownership, and drive projects from design through production.
Strong English communication skills and the ability to collaborate across multiple engineering teams.
AI Experience you have:
Hands-on experience using AI and Generative AI tools as part of software development, infrastructure, or operational workflows.
Experience working with LLM APIs, AI coding assistants, or AI automation tools.
Ability to identify processes that can be improved through AI and translate them into practical engineering solutions.
Understanding of prompt engineering, responsible AI usage, and the secure handling of sensitive information.
Experience with CAG, AI agents, RAG, MCP, LLM integrations, or AIOps solutions is an advantage.
Bonus points:
Experience working with data platforms, data infrastructure, or large-scale data processing systems.
Understanding of data pipelines, data ingestion, streaming systems, or distributed data architectures.
Experience operating large-scale, multi-region, or multi-account AWS environments.
Experience with GitOps tools such as Argo CD or Akuity.
Experience with service mesh, API gateways, or service discovery solutions.
Experience with Datadog, Prometheus, Grafana, OpenTelemetry, or similar observability tools.
Experience with cloud security, secrets management, and Vault.
