AI Infrastructure Architect

As a hands-on Engineer in AI Infrastructure Architecture, you will design, build, automate, monitor, and optimize AI/ML infrastructure on Google Cloud Platform for reliable, scalable, and cost-effective model development and production workloads. You will work on moderately complex infrastructure components under guidance from senior architects and engineers, contributing to accelerated compute environments, model deployment pipelines, observability, security, and operational reliability for AI-driven business solutions.

Responsibilities:

• Write, review and debug code, scripts and infrastructure-as-code for GCP AI infrastructure, automation, monitoring and deployment tooling.
• Configure and provision GCP compute resources for AI/ML workloads, including Compute Engine, Google Kubernetes Engine, Vertex AI, Cloud Storage and supporting networking/security services.
• Support deployment automation and CI/CD pipelines for AI systems, models and applications using tools such as Git, Terraform, Cloud Build, Docker, Kubernetes and workflow orchestration tooling.
• Deploy and operate AI services, model-serving components and data pipelines while applying reliability, security, cost-efficiency and scalability practices.
• Monitor infrastructure and model-serving health using Cloud Monitoring, Cloud Logging and related observability tools, and troubleshoot issues across compute, storage, networking, containers and application layers.
• Collaborate with data scientists, ML engineers, platform engineers and architects to integrate AI models into enterprise systems while meeting compliance and operational requirements.
• Document reusable patterns, configuration standards and runbooks for GCP-based AI infrastructure.

Requirements:

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
• Minimum 2 years of experience coding, building, monitoring or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines or cloud/platform engineering solutions.
• Strong understanding of AI/ML concepts and the compute, storage, networking, security and deployment foundations required to run AI workloads.
• Minimum 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash or PowerShell.
• Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration and operational monitoring tools.
• Strong problem-solving ability, communication skills and collaboration mindset in a fast-paced engineering environment.
• Hands-on experience with GCP services relevant to AI infrastructure such as Compute Engine, GKE, Vertex AI, Cloud Storage, IAM, VPC, Cloud Build, Cloud Monitoring and Cloud Logging.
• Experience designing or operating accelerated compute, distributed training setups, containerized deployments and model-serving workloads.
• Working knowledge of Terraform, Docker, Kubernetes, CI/CD pipelines and observability practices.
• Ability to optimize infrastructure for performance, reliability, scalability, cost and security.
• Understanding of MLOps patterns including experiment tracking, model registry, model deployment, monitoring and rollback approaches.
• GCP certification such as Associate Cloud Engineer, Professional Cloud Architect, Professional Data Engineer or Professional Machine Learning Engineer.
• Exposure to industry use cases in BFSI, healthcare, retail/e-commerce, telecom, manufacturing or public sector where AI infrastructure must meet compliance, reliability and data-governance expectations.
• Familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling, or model optimization techniques.
• Knowledge of security controls, FinOps practices, incident management and production support processes for enterprise AI platforms.
• 15 years of full-time education.