3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define the Azure AI infrastructure vision, strategy, reference architecture, standards, and implementation strategy for large-scale AI/ML systems.
- Make architectural decisions across compute, networking, storage, orchestration, model serving, observability, security, reliability, and cost optimization.
- Architect and prototype GPU-accelerated compute and distributed training environments using benchmarks, proofs of concept, and reusable implementation patterns.
- Define infrastructure-as-code, CI/CD, model deployment, monitoring, SLA/SLO, and cost/performance governance patterns.
- Lead architecture assessments and design reviews, validating findings through hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate emerging AI infrastructure technologies, accelerators, interconnects, managed AI services, vendors, frameworks, and platform services.
- Provide executive- and client-level technical advisory that connects infrastructure trade-offs to business outcomes.
- Lead AI infrastructure projects and teams through technical direction, delivery governance, stakeholder alignment, and design reviews.
- Mentor architects and Technical Architects and represent the practice in technical forums.
Requirements
- Minimum 15 years of experience is stated for the project role; the qualifications section also requires a minimum of 6 years in AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale architecture solutions.
- Bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related technical field.
- At least 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Expert hands-on experience with Azure AI infrastructure services including Azure VMs, Azure Kubernetes Service, Azure Machine Learning, Azure Storage, Azure Virtual Network, Entra ID, Azure Monitor, and Azure DevOps or GitHub Actions.
- Strong understanding of AI/ML concepts, GPU-accelerated compute, distributed training, model serving, container platforms, storage and network design, observability, resilience, and production deployment.
- Experience with Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tools.
- Strong experience with Bicep, ARM, or 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 technologies and reference architectures, and producing standards, decision records, benchmarks, and executive recommendations.
- Preferred qualifications include Azure certifications, industry experience in regulated or enterprise sectors, exposure to LLM infrastructure and vector databases, and experience with enterprise governance, roadmaps, vendor management, FinOps, and production support.
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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.