3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Set the GCP AI infrastructure vision, strategy, and reference architecture for large-scale AI/ML systems.
- Own architectural decisions across compute, networking, storage, orchestration, model serving, observability, security, and cost optimization using GCP services.
- Architect and prototype cost-optimized accelerated compute and distributed training environments, including benchmarks and proof-of-concepts.
- Define infrastructure-as-code patterns, deployment patterns, monitoring strategies, service objectives, and production cost/performance governance.
- Lead architecture assessments and design reviews using hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate AI infrastructure technologies, accelerators, interconnects, managed AI services, vendors, and ecosystem tools.
- Provide executive and client-level technical advisory on cloud infrastructure trade-offs and business outcomes.
- Lead AI infrastructure projects and teams, mentor architects, establish best practices, and represent the practice in technical forums.
Requirements
- A bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related technical field.
- The posting states that a minimum of 18 years of experience is required.
- At least 6 years of experience building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale architecture solutions.
- At least 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Expert 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.
- Strong knowledge of accelerated compute, distributed training, model serving, container platforms, storage and network design, observability, and resilience.
- Experience with data pipeline and workflow tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Strong experience with Terraform, Docker, Kubernetes, security guardrails, monitoring, and infrastructure cost optimization.
- Experience applying MLOps and InfraOps practices to experiment tracking, model registries, deployment automation, monitoring, incident response, and rollback strategies.
- Proven experience leading AI infrastructure projects and teams, conducting design reviews, managing delivery governance, and aligning stakeholders.
- Experience evaluating and selecting AI technologies, frameworks, reference architectures, and platform services for production solutions.
- Preferred qualifications include GCP certifications, industry experience in regulated or enterprise sectors, exposure to LLM infrastructure and vector databases, and experience with enterprise architecture governance, roadmaps, 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.