Project Role : AI Infrastructure Architect

Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.

Must have skills : Machine Learning Operations

Good to have skills : Google Cloud Data Services

Minimum 5 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

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.

Key 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 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.

Required Qualifications

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.

Required Skills/ Experience

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.

Good to Have Skills

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.