Senior Platform Architect

Accountabilities:

As a Senior Platform Architect, you will define and improve how cloud platforms are designed, deployed, operated, and scaled. You will combine strong SRE and DevOps practices with hands-on infrastructure engineering to create reliable platforms capable of supporting complex backend, AI, and multi-tenant workloads.

• Design, build, and operate scalable backend and AI infrastructure supporting real-time and batch workloads.
• Establish architectural patterns that prioritize performance, reliability, scalability, and multi-tenant operation.
• Design and maintain deployment workflows covering versioning, staged rollouts, automated releases, monitoring, and safe rollback strategies.
• Build and operate production LLM and agentic systems, integrating model providers, APIs, gateways, tools, and external services.
• Manage reliability considerations for AI workloads, including rate limits, quotas, guardrails, graceful degradation, and operational resilience.
• Develop reusable services, APIs, automation, and data pipelines supporting AI-powered products and internal platform capabilities.
• Extend infrastructure-as-code practices using Terraform, reusable modules, and consistent provisioning patterns.
• Maintain and improve GitOps-based deployment workflows using tools such as ArgoCD or Flux.
• Operate Kubernetes workloads in production, including scaling, workload placement, tenant isolation, reliability, and infrastructure capacity management.
• Improve observability through metrics, logging, tracing, SLOs, alerting, incident response, and operational tooling.
• Identify opportunities to improve infrastructure performance, cloud efficiency, and AI workload costs.
• Build automation that eliminates repetitive manual processes and improves engineering productivity.
• Use agentic coding and AI development tools to accelerate development, infrastructure work, debugging, code review, and workflow automation.
• Define operational standards and contribute to architectural decisions that support continued platform growth.

Requirements:

The ideal candidate is a senior platform, SRE, or infrastructure engineer with substantial experience operating production systems at scale. You should combine deep technical expertise with strong architectural judgment, automation-first thinking, and the ability to communicate complex operational decisions clearly to both technical teams and leadership.

• 5+ years of experience in platform engineering, SRE, infrastructure, or a closely related discipline.
• Significant hands-on experience operating production systems at scale.
• Strong SRE and DevOps foundation, including ownership of reliability, SLOs, post-mortems, incident management, and measurable improvements.
• Deep hands-on expertise with Terraform, including complex state management, reusable modules, multi-project configurations, and CI-driven plan/apply workflows.
• Strong production experience with GitOps tools such as ArgoCD or Flux and a deep understanding of declarative infrastructure management.
• Advanced Kubernetes knowledge, including production cluster operations and troubleshooting real-world failure scenarios.
• Strong experience with at least one major public cloud platform, such as AWS, Azure, or GCP, including networking, compute, IAM, storage, and multi-account or multi-project architectures.
• Hands-on experience building and operating CI/CD pipelines using GitHub Actions, Cloud Build, GitLab CI, or equivalent technologies.
• Strong automation-first mindset, with experience designing systems that reduce manual operational work.
• Active experience using agentic coding and AI development tools, with the ability to direct them effectively and critically review their output.
• Excellent communication skills and the ability to clearly explain operational decisions, technical trade-offs, and incident outcomes.
• MLOps experience and hands-on experience deploying or operating ML/AI workloads in production is a strong advantage.
• Strong GCP experience, particularly with VPC networking, Compute Engine, IAM, Cloud Storage, multi-project or organization design, and GKE, is a plus.
• Experience with GCP data services such as BigQuery, Dataflow, Pub/Sub, or Dataproc is advantageous.
• Experience with GPU or accelerator scheduling and node lifecycle management is a plus.
• Experience operating LLM inference at scale, including quotas, throttling, gateways, caching, and guardrails, is advantageous.
• Familiarity with ML orchestration tools such as Argo Workflows, Kubeflow, Airflow, Vertex AI Pipelines, or equivalent technologies is a plus.
• Experience with model registries, feature stores, experiment tracking, ML observability, or data and model drift monitoring is beneficial.
• Background in FinOps, cloud cost attribution, committed-use discounts, reservations, or accelerator capacity planning is advantageous.
• Experience with multi-tenant infrastructure, including isolation, noisy-neighbor mitigation, and tenant lifecycle management, is a plus.
• Previous experience scaling platform or ML infrastructure within a growing startup or enterprise environment is beneficial.

Benefits:

• Fully remote opportunity within LATAM.
• Compensation paid in USD.
• Working hours aligned with the EST time zone.
• Opportunity to work on modern cloud platforms and production-scale infrastructure.
• Hands-on exposure to LLMs, agentic systems, and AI-powered workloads.
• Opportunity to shape platform architecture and engineering standards.
• High level of technical ownership and autonomy.
• Opportunity to work with Kubernetes, Terraform, GitOps, CI/CD, and modern SRE practices.
• Exposure to complex scalability, reliability, observability, and cloud cost challenges.
• Opportunity to use modern AI-assisted engineering and agentic coding tools.
• Environment focused on continuous platform evolution rather than maintaining the status quo.