1 day ago
Bengaluru, IndiaStaff+
Responsibilities
- Set the GCP AI infrastructure vision, strategy, and reference architecture for large-scale AI/ML systems.
- Own architecture decisions across compute, networking, storage, orchestration, model serving, observability, security, and cost optimization.
- Design and prototype cost-optimized accelerated compute and distributed training environments, including benchmarks and proofs of concept.
- Define infrastructure-as-code, deployment, monitoring, reliability, service-level, and cost/performance governance standards.
- Lead architecture assessments and design reviews with hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate emerging AI infrastructure technologies, accelerators, managed AI services, and ecosystem tools.
- Provide executive and client-level technical advisory tied to business outcomes.
- Lead AI infrastructure projects and teams, mentor architects, and develop technical community best practices.
Requirements
- A bachelor's degree in Computer Science, Information Technology, or a related technical field is required.
- At least 15 years of experience is stated as required; the qualifications section also specifies at least 6 years building, operating, and architecting AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale technical architecture solutions.
- At least 6 years of programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell is required.
- Expert hands-on experience with GCP AI infrastructure services, including Compute Engine, GKE, Vertex AI, Cloud Storage, IAM, VPC, Cloud Build, Cloud Monitoring, and Cloud Logging.
- Strong knowledge of accelerated compute, distributed training, model serving, containers, storage and network design, observability, resilience, and infrastructure cost optimization.
- Experience with Terraform, Docker, Kubernetes, data and workflow orchestration tools such as Apache Airflow or Kubeflow, and MLOps/InfraOps practices.
- Experience leading AI infrastructure projects and teams, conducting design reviews, managing delivery governance, and aligning stakeholders.
- Experience evaluating AI technologies, frameworks, reference architectures, and platform services for production use.
- Preferred qualifications include relevant GCP certifications, industry experience in regulated or enterprise sectors, exposure to LLM infrastructure and vector databases, and experience with enterprise architecture governance, vendor management, FinOps, and production support models.
Tech Stack
Categories
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.