Senior Platform Architect

Wizdaa · posted today ago

Listing supplied by Himalayas. 101 Careers did not originate this post and applications are handled by the employer.

About this role

We're looking for a Platform Architect who can set the standard for how we build, ship, and operate reliable cloud platforms at scale. You sit at the intersection of platform engineering and SRE. You'll own the path from infrastructure design to reliable production services, bringing DevOps rigor to complex systems.

This is not a ticket-processing role, and it's not a research role. You'll tackle hard problems: platform reliability, scalability, cost efficiency, deployment automation, and workload operations. You'll have the scope to solve them properly. Senior professionals here identify problems before they're asked and raise the ceiling on what the platform can do.

What you will work on

Build and operate scalable backend and AI infrastructure, supporting real-time and batch workloads with a focus on performance, reliability, and multi-tenant architecture.
Design and maintain deployment workflows across services and environments, including versioning, staged rollouts, automated releases, monitoring, and safe rollback strategies.
Build and operate LLM and agentic systems in production, integrating model providers, APIs, gateways, tools, and external services while managing rate limits, reliability, guardrails, and graceful degradation.
Develop reusable services, APIs, automation, and data pipelines that support AI-powered products and internal platform capabilities.
Extend infrastructure-as-code across the platform using Terraform and reusable patterns to provision and manage cloud services consistently across projects and environments.
Maintain GitOps-based deployment workflows using tools such as ArgoCD, improving automation and consistency across environments and tenants.
Run distributed workloads on Kubernetes (GKE), managing scaling, workload placement, tenant isolation, service reliability, and infrastructure capacity.
Improve platform observability and reliability through metrics, logging, tracing, SLOs, alerting, incident response practices, and operational tooling.
Identify performance and infrastructure cost improvements across cloud services, compute resources, APIs, and AI workloads.
Use agentic coding and AI development tools to accelerate engineering work, including scaffolding services, generating and reviewing infrastructure and application code, debugging, and automating repetitive workflows.

What you won’t find here

A platform team that maintains the status quo. We're actively building: new scale requirements, new architectural domains, and an ML/AI footprint that's growing fast. Senior engineers here shape how the platform evolves, and the tools available to do it are better than they've ever been.

Requirements

Must have

5+ years in platform engineering, SRE, or infrastructure, with meaningful time operating production systems at scale.
Strong SRE/DevOps foundation. You've owned reliability for production services, defined and measured SLOs, run post-mortems, and driven measurable improvements.
Deep Terraform expertise. You actively manage complex Terraform state, reusable modules, and multi-project configurations in production, with CI-driven plan/apply workflows.
Strong GitOps background (ArgoCD or Flux in production). You understand declarative infrastructure management at depth and have opinions on how to do it well.
Deep Kubernetes knowledge. You've operated clusters in production, dealt with real failure modes, and understand the system at the control plane level.
Strong cloud infrastructure background across at least one major public cloud (AWS, Azure, or GCP), including networking, compute, IAM, storage, and multi-account or multi-project design.
Hands-on experience building and operating CI/CD pipelines (GitHub Actions, Cloud Build, GitLab CI, or equivalent).
Automation-first thinking at a senior level. You implement systems that eliminate entire categories of manual work.
Active user of agentic coding tools. You know how to direct them effectively, review their output critically, and use them to multiply your output.
Strong communicator. You can articulate operational decisions, technical trade-offs, and incident summaries clearly to engineers and leadership alike.

Nice to have

MLOps experience, including hands-on experience deploying and operating ML or AI workloads in production.
Strong GCP experience, including VPC networking, Compute Engine, IAM, Cloud Storage, multi-project or organization design, and GKE (Standard and/or Autopilot).
Hands-on experience with GCP data services, especially BigQuery in production: partitioning and clustering, query cost and performance tuning, and dataset-level IAM. Familiarity with at least one of Dataflow, Pub/Sub, or Dataproc.
Experience with GPU/accelerator scheduling and node lifecycle management in production (e.g., GKE node auto-provisioning, GPU time-sharing, or equivalent).
Experience operating LLM inference at scale, managing provider quotas/throttling (TPS/TUPS), gateways, caching, and guardrails (e.g., Vertex AI, Gemini API, or equivalent).
Experience with ML pipeline and orchestration tooling such as Argo Workflows, Kubeflow, Cloud Composer/Airflow, Vertex AI Pipelines, or equivalent.
Experience with model registries, feature stores, and experiment tracking (e.g., MLflow, Feast, or equivalent).
Familiarity with model and data drift monitoring and ML-specific observability.
Background in FinOps: inference cost attribution, committed use discount (CUD) and reservation planning, and accelerator capacity forecasting.
Familiarity with data infrastructure such as object storage, CDC pipelines, or lakehouse patterns.
Experience with multi-tenant infrastructure: isolation patterns, noisy neighbor mitigation, and tenant lifecycle management.
Prior experience scaling ML or platform infrastructure at a startup moving toward enterprise-grade requirements.