The Role
Design, build, automate, monitor, and optimize AI/ML infrastructure on Google Cloud. Configure compute, storage, networking, security, containers, Kubernetes, CI/CD pipelines, model-serving systems, and data platforms. Improve performance, reliability, scalability, cost, and security while supporting MLOps capabilities such as model deployment, monitoring, registries, rollback, and experiment tracking. Collaborate with data scientists, ML engineers, platform engineers, and architects to deliver production AI systems.
Summary Generated by Built In
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.15 years full time education
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.15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Skills Required
- Minimum 5 years of professional experience
- 15 years of full-time education
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field
- At least 2 years of experience coding, building, monitoring, or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions
- At least 2 years of proficiency in Python, Java, C++, Bash, or PowerShell
- Strong understanding of AI/ML concepts and infrastructure foundations including compute, storage, networking, security, and deployment
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational monitoring
- Hands-on experience with GCP services including Compute Engine, GKE, Vertex AI, Cloud Storage, IAM, VPC, Cloud Build, Cloud Monitoring, and Cloud Logging
- Experience with accelerated compute, distributed training, containerized deployments, and model-serving workloads
- Working knowledge of Terraform, Docker, Kubernetes, CI/CD pipelines, and observability practices
- Ability to optimize AI infrastructure for performance, reliability, scalability, cost, and security
- Understanding of MLOps patterns including experiment tracking, model registries, deployment, monitoring, and rollback
- Strong problem-solving, communication, and collaboration skills
- GCP certification such as Associate Cloud Engineer, Professional Cloud Architect, Professional Data Engineer, or Professional Machine Learning Engineer
- Experience with BFSI, healthcare, retail/e-commerce, telecom, manufacturing, or public-sector AI use cases
- Familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling, or model optimization
- Knowledge of security controls, FinOps, incident management, and production support processes
Accenture Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Accenture and has not been reviewed or approved by Accenture.
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Healthcare Strength — Pay is considered competitive when paired with robust insurance options and other perks that compare well with large consulting and IT services peers. Multiple national medical plan options plus dental and vision are positioned as a core strength of the overall package.
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Retirement Support — Retirement support is positioned as a standout feature through a 401(k) dollar-for-dollar match up to a set percentage after eligibility. The package is reinforced by additional financial programs such as savings tools and related resources.
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Parental & Family Support — Parental and caregiving supports are presented as a meaningful benefit differentiator through substantial paid parental leave and multiple caregiver-oriented programs. Backup care and fertility/adoption/surrogacy navigation and reimbursements add breadth to family support beyond leave alone.
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The Company
What We Do
Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services—all powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. Our 500,000+ people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com.
