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Accenture

Advises businesses and runs their technology

AI Infrastructure Architect

Cloud EngineeringUpdated on Nov 11
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related Technical Architecting field is required.
  • At least 15 years of experience is required for the project role.
  • At least 6 years of experience coding, building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale architecture solutions.
  • Strong understanding of AI/ML concepts and the compute, infrastructure, orchestration, and deployment foundations required to run production AI systems.
  • At least 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent languages.
  • Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
  • Proven experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance, and stakeholder alignment.
  • Strong project management, problem-solving, and cross-functional collaboration skills in client or enterprise environments.
  • Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures, and platform services for production solutions.
  • Expert-level hands-on architecture experience with GCP AI infrastructure services, including Compute Engine, GKE, Vertex AI, Cloud Storage, IAM, VPC, Cloud Build, Cloud Monitoring, and Cloud Logging.
  • Deep knowledge of accelerated compute, distributed training, model serving, container platforms, storage design, network design, observability, and resilience architecture.
  • Strong experience with Terraform, CI/CD, Docker, Kubernetes, security guardrails, monitoring, and infrastructure cost optimization.
  • Ability to evaluate multiple GCP architecture options and produce standards, patterns, decision records, benchmarks, and executive-ready recommendations.
  • Experience applying MLOps/InfraOps practices for experiment tracking, model registry, deployment automation, monitoring, incident response, and rollback strategies.

Responsibilities

  • Set the overarching GCP AI infrastructure vision, strategy, and reference architecture for large-scale AI/ML systems, including compute, networking, storage, orchestration, model serving, and observability.
  • Own complex architectural decisions across GCP services such as Compute Engine, GKE, Vertex AI, Cloud Storage, Filestore, VPC, IAM, Cloud Build, Cloud Monitoring, and Cloud Logging, rationalizing options against client standards and business objectives.
  • Architect and prototype cost-optimized accelerated compute and distributed training environments, building benchmarks, proof-of-concepts, and reusable implementation patterns.
  • Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, model deployment patterns, monitoring strategy, SLAs/SLOs, and cost/performance governance for production AI/ML systems.
  • Lead architecture assessments and design reviews, and validate findings through hands-on implementation, profiling, performance tuning, and troubleshooting across the AI infrastructure stack.
  • Evaluate emerging AI infrastructure technologies, accelerators, interconnects, managed AI services, and ecosystem tools, and recommend where they belong in enterprise solutions.
  • Provide executive- and client-level technical advisory, translating cloud infrastructure trade-offs into clear, defensible recommendations connected to business outcomes.
  • Mentor architects and Technical Architects, build community best practices, and represent the practice in internal and external technical forums.

Desired Qualifications

  • GCP certifications such as Professional Cloud Architect, Professional Data Architect, Professional Machine Learning Engineer, or Professional Cloud DevOps Engineer.
  • Industry experience designing AI infrastructure for BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy, or public-sector environments with compliance, security, and reliability constraints.
  • Exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, high-performance storage, and low-latency model serving.
  • Experience with enterprise architecture governance, technology roadmaps, vendor/partner management, FinOps, and production support operating models.
  • Google Cloud Data Services experience is listed as a good-to-have skill.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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