Senior Engineer - Cloud Platforms

Design, build, and operate highly available, scalable AWS infrastructure — including VPC architecture, IAM policy management, ECS/EKS container workloads, Lambda functions, RDS/Aurora databases, and S3 storage — in alignment with security and compliance requirements.

Lead Infrastructure as Code delivery: develop and maintain production-grade Terraform/OpenTofu modules, enforce IaC standards across the team, manage state backends, and drive toward 100% IaC coverage for owned infrastructure.

Build and optimize CI/CD pipelines in Harness — including deployment strategies, automated verification gates, rollback workflows, and pipeline-as-code standards that support frequent, low-risk releases.

Implement and maintain cloud security controls across owned systems: IAM least-privilege policies, KMS encryption, secrets management (AWS Secrets Manager/Vault), security group governance, and alignment with SOC2 and applicable compliance frameworks.

Provision and support AI/ML infrastructure on AWS — including SageMaker endpoints, Bedrock AgentCore integrations, and GPU-optimized compute — enabling the engineering team to build and deploy AI agent workflows reliably.

Monitor and tune production systems using CloudWatch, Datadog, or equivalent observability tooling — defining SLOs, reducing alert noise, and ensuring on-call engineers have high-confidence signals to act on.

Automate operational workflows through Python, Bash, or Go scripting — including infrastructure provisioning, configuration drift remediation, security patching, and incident response runbooks.

Use AI coding assistants (Claude Code and GitHub Copilot) as force-multipliers in your daily workflow — drafting Terraform modules, automation scripts, and pipeline configuration, while applying the same critical review to AI-generated output as you would to any peer pull request. Hold AI-generated code and documentation to the same quality bar as human-authored work: catch hallucinations, security anti-patterns, and logic errors before they reach staging or production.

Design and test disaster recovery procedures for owned services: backup validation, cross-region failover, and RTO/RPO documentation aligned to business requirements.

Bachelor's Degree in Computer Science, Engineering, Information Systems, or related discipline, or equivalent experience.

Minimum of 7 years of experience in cloud infrastructure, DevOps, or SRE roles, with at least 3 years of hands-on AWS production experience at meaningful scale.

Deep, practical AWS expertise — EC2, VPC, IAM, RDS/Aurora, ECS/EKS, Lambda, S3, CloudWatch, Route 53, KMS — with the ability to architect, not just operate.

Production-level Terraform or OpenTofu proficiency: module development, remote state management, workspace strategies, and code review for IaC contributions.

CI/CD pipeline design and operation experience — Harness strongly preferred; GitHub Actions or Jenkins also valued.

Container orchestration with Kubernetes (EKS preferred) and Docker, including pod scheduling, resource management, and network policy configuration.

Scripting and automation proficiency in Python, Bash, or Go — used for real operational tooling, not just one-off tasks.

Linux/Unix systems fundamentals: networking (VPC design, load balancing, DNS, security groups), storage, and process management in production environments.

Experience monitoring and operating production systems with CloudWatch, Datadog, Prometheus/Grafana, or equivalent observability stacks.

Relevant certifications valued: AWS Solutions Architect Associate or Professional, HashiCorp Terraform Associate, Certified Kubernetes Administrator. Preferred but not required.

Participates in a formal on-call rotation, serving as a primary responder for critical infrastructure incidents outside of business hours.

Strong communicator who can translate infrastructure decisions into developer-friendly guidance and clearly document architecture choices for technical and non-technical stakeholders.

Ownership mentality: you treat the infrastructure you build the same way you'd treat production — with rigor, documentation, and an eye toward the engineer who inherits it.

Familiarity with AI coding assistants (Claude Code, GitHub Copilot, or equivalent) as active workflow tools — directing them for complex tasks, with the judgment to know when to trust the output and when to rewrite it.