3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define the GCP AI infrastructure vision, strategy, reference architecture, standards, and implementation approach for large-scale AI/ML systems.
- Architect compute, networking, storage, orchestration, model serving, observability, security, resilience, and cost-optimization solutions using GCP services.
- Prototype accelerated compute and distributed training environments through benchmarks, proofs of concept, profiling, performance tuning, and troubleshooting.
- Define infrastructure-as-code patterns, CI/CD approaches, model deployment patterns, monitoring strategies, SLAs/SLOs, and cost/performance governance.
- Lead architecture assessments, design reviews, AI infrastructure projects, delivery governance, and stakeholder alignment.
- Evaluate AI infrastructure technologies, accelerators, interconnects, managed AI services, vendors, frameworks, and platform services.
- Provide executive- and client-level technical advisory connected to business outcomes.
- Mentor architects and Technical Architects and develop internal and external technical best practices.
Requirements
- A bachelor's degree in Computer Science, Information Technology, or a related technical field is required.
- A minimum of 15 years of experience is required, including experience building, designing, monitoring, troubleshooting, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale 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, container platforms, storage and network design, observability, resilience, and production AI systems.
- Experience with Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Strong experience with Terraform, CI/CD, Docker, Kubernetes, security guardrails, monitoring, and infrastructure cost optimization.
- Experience applying MLOps or InfraOps practices for experiment tracking, model registries, deployment automation, monitoring, incident response, and rollback strategies.
- Proven experience leading AI infrastructure projects and teams, evaluating AI technologies and reference architectures, and producing standards, patterns, decision records, benchmarks, and executive-ready recommendations.
- Strong project management, communication, problem-solving, and cross-functional collaboration skills.
- Preferred qualifications include relevant GCP certifications, industry experience in regulated or enterprise sectors, exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, high-performance storage, low-latency model serving, enterprise architecture governance, technology roadmaps, vendor management, FinOps, and production support operating 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.