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 : Google Cloud Data Services

Good to have skills : Machine Learning Operations, AI Agents & Workflow Integration

Minimum 7.5 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

As a Senior Engineer in AI Infrastructure Architecture for GCP, you will own significant portions of the end-to-end architecture and engineering of optimized compute infrastructure for large-scale AI and machine learning systems. You will design scalable distributed training environments, model-serving foundations, automation patterns and operational controls that align with client standards, SLAs, security, compliance and cost-efficiency expectations. You will bring industry experience across enterprise AI adoption, cloud modernization, regulated workloads, FinOps and production reliability, while mentoring engineers and partnering with architects to translate business requirements into robust GCP-based AI infrastructure solutions.

Key Responsibilities

Own end-to-end architecture and design of optimized GCP compute infrastructure for large-scale AI/ML systems, including distributed training, accelerated compute, container platforms and model-serving environments.

Design and tune large-scale GCP accelerated compute clusters and distributed training systems using services such as Compute Engine, GKE, Vertex AI, Cloud Storage, Filestore, VPC, IAM, Cloud Build, Cloud Monitoring and Cloud Logging, including accelerator selection, networking and high-throughput storage design.

Serve as an authoritative AI infrastructure expert on GCP, applying deep knowledge of GCP AI/ML services, accelerators, networking, security and cost levers.

Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost and operational complexity.

Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks and optimization opportunities, and recommending remediation actions.

Drive architecture decision-making by documenting rationale, trade-offs, assumptions and dependencies so decisions are transparent, defensible and aligned with business SLAs and standards.

Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts and performance improvement opportunities.

Design deployment, automation and CI/CD strategies for reliable, repeatable and scalable releases of AI systems, models, data pipelines and platform components into production.

Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, performance/cost tracking and continuous optimization.

Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, compliance, regulatory alignment and adherence to client standards.

Collaborate with clients, stakeholders, architects and engineering teams to align infrastructure decisions with business outcomes and translate requirements into actionable architecture standards.

Set technical direction for workstreams, mentor engineers, review designs/code and promote engineering best practices across the team.

Required Qualifications

Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.

Minimum 4 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale engineering solutions.

Strong understanding of AI/ML concepts and the computing infrastructure required to deploy, run and optimize production AI workloads.

Minimum 4 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent engineering languages.

Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.

Strong problem-solving skills and ability to work in a fast-paced engineering or client delivery environment.

Excellent communication, collaboration and stakeholder alignment skills.

Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform for deploying large-scale solutions.

Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.

Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services and architecture patterns.

Required Skills/ Experience

Strong hands-on experience with GCP AI infrastructure services including Compute Engine, GKE, Vertex AI, Cloud Storage, Filestore, IAM, VPC, Cloud Build, Cloud Monitoring and Cloud Logging.

Experience architecting accelerated compute, distributed training, model serving, high-throughput storage, container platforms and secure cloud networking.

Strong working knowledge of Terraform, CI/CD, Docker, Kubernetes, InfraOps, MLOps, observability and incident response practices.

Ability to optimize GCP AI infrastructure for performance, power, cost, scalability, security, reliability and compliance.

Experience producing architecture decision records, reference implementations, standards, runbooks and reusable infrastructure patterns.

Good to Have Skills

GCP certifications such as Professional Cloud Architect, Professional Data Engineer, Professional Machine Learning Engineer or Professional Cloud DevOps Engineer.

Industry experience in BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments where AI infrastructure must meet compliance, security, reliability and cost-control requirements.

Exposure to LLM infrastructure, vector databases, retrieval pipelines, accelerator scheduling, high-performance storage, low-latency model serving and model optimization techniques.

Knowledge of enterprise architecture governance, FinOps, infrastructure partner/vendor collaboration and production support operating models.