Roles & Responsibilities

Define and maintain the reference architecture for an enterprise AI/ML platform, including data pipelines, model training, CI/CD, and observability.
Build reusable platform services like model registries, training environments, orchestration frameworks, and experiment tracking systems.
Develop self-service interfaces (API/CLI/UI) for scalable, secure platform consumption by engineering teams.
Collaborate across AI/ML teams, enterprise architects, and product engineering to ensure platform integration and adoption.
Design deployment strategies for cloud, internal APIs, dashboards, and embedded edge systems.
Implement governance and technical standards for lifecycle management, performance, and reusability.
Serve as a technical leader, contributing to platform strategy, roadmap, and architecture best practices.

Key Skills Required

10+ years in Software / ML Platform / Infrastructure Engineering
3+ years in AI Platform or MLOps Architecture roles
Strong hands-on experience with GCP cloud-native tools: Vertex AI, GKE, Cloud Run, Artifact Registry, Pub/Sub
Experience with CI/CD for ML (e.g., GitHub Actions, Kubeflow, Terraform)
Strong skills in Kubernetes, Docker, and secure container management
Deep understanding of model lifecycle management (training, versioning, deployment, monitoring)
Familiarity with tools like Airflow, feature stores, dataset management
Excellent architectural and system design capabilities
Proven ability to work across multiple teams and influence technical decisions

AI Platform Architect