3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define the Azure AI infrastructure vision, strategy, reference architecture, standards, and implementation approach for large-scale AI/ML systems.
- Own architecture decisions across compute, networking, storage, orchestration, model serving, observability, security, resilience, and cost optimization.
- Architect and prototype GPU-accelerated compute and distributed training environments, including benchmarks, proofs of concept, and reusable implementation patterns.
- Define infrastructure-as-code, CI/CD, model deployment, monitoring, SLA/SLO, and cost/performance governance patterns for production AI/ML systems.
- Lead architecture assessments and design reviews, validating findings through hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate emerging AI infrastructure technologies, accelerators, managed AI services, vendors, and ecosystem tools for enterprise adoption.
- Provide executive and client technical advisory and translate infrastructure trade-offs into business-focused recommendations.
- Mentor architects, establish community best practices, and represent the practice in technical forums.
Requirements
- Minimum 18 years of overall experience is required.
- 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 technical architecture solutions.
- At least 6 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Bachelor's-level education in Computer Science, Information Technology, or a related technical field, with 15 years of full-time education stated.
- Strong understanding of AI/ML concepts and the infrastructure, orchestration, compute, and deployment foundations required for production AI systems.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Expert hands-on architecture experience with Azure VMs, Azure Kubernetes Service, Azure Machine Learning, Azure Storage, Azure Virtual Network, Entra ID, Azure Monitor, and Azure DevOps or GitHub Actions.
- Strong experience with Bicep, ARM, Terraform, Docker, Kubernetes, monitoring, security guardrails, CI/CD, 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, conducting design reviews, managing delivery governance, and aligning stakeholders.
- Demonstrated ability to evaluate AI technologies, frameworks, reference architectures, and platform services for production solutions.
- Strong project management, communication, problem-solving, and cross-functional collaboration skills.
- Preferred experience includes Azure architecture, DevOps, AI, or data certifications; regulated-industry AI infrastructure; LLM infrastructure; vector databases; retrieval pipelines; GPU scheduling; model optimization; high-performance storage; low-latency model serving; enterprise architecture governance; vendor management; FinOps; and production support operating models.
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.