AI Platform Architect

Roles & Responsibilities

• Define and maintain thereference architecturefor an enterprise AI/ML platform, including data pipelines, model training, CI/CD, and observability.
• Build reusableplatform serviceslike model registries, training environments, orchestration frameworks, and experiment tracking systems.
• Developself-service interfaces (API/CLI/UI)for scalable, secure platform consumption by engineering teams.
• Collaborate acrossAI/ML teams, enterprise architects, and product engineeringto ensure platform integration and adoption.
• Design deployment strategies forcloud, internal APIs, dashboards, and embedded edge systems.
• Implementgovernance and technical standardsfor lifecycle management, performance, and reusability.
• Serve as atechnical leader, contributing to platform strategy, roadmap, and architecture best practices.

Key Skills Required

• 10+ years inSoftware / ML Platform / Infrastructure Engineering
• 3+ years inAI Platform or MLOps Architectureroles
• Strong hands-on experience withGCP cloud-native tools: Vertex AI, GKE, Cloud Run, Artifact Registry, Pub/Sub
• Experience withCI/CD for ML(e.g., GitHub Actions, Kubeflow, Terraform)
• Strong skills inKubernetes, Docker, and secure container management
• Deep understanding ofmodel lifecycle management(training, versioning, deployment, monitoring)
• Familiarity with tools likeAirflow,feature stores, dataset management
• Excellentarchitectural and system designcapabilities
• Proven ability to work across multiple teams and influence technical decisions