3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Architect and build optimized Azure infrastructure for large-scale AI and machine learning systems.
- Design and tune Azure GPU clusters, distributed training systems, model-serving environments, container platforms, networking, and high-throughput storage.
- Evaluate architecture alternatives and lead reviews of existing and proposed environments, identifying risks, bottlenecks, and optimization opportunities.
- Define infrastructure roadmaps, capacity planning models, scaling strategies, cost forecasts, and performance improvements.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish MLOps and InfraOps monitoring, observability, SLAs, SLOs, alerting, and performance and cost tracking.
- Ensure enterprise AI/ML integrations meet security, compliance, regulatory, interoperability, and client-standard requirements.
- Produce architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Set technical direction, lead engineering workstreams, mentor engineers, and review designs and code.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- At least 7.5 years of experience is required, including experience in AI/ML infrastructure engineering or related large-scale cloud or enterprise platform roles.
- At least 4 years of experience coding, building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale engineering solutions.
- At least 4 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Strong understanding of AI/ML concepts and infrastructure for production AI workloads.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Hands-on experience with Azure VMs, AKS, Azure Machine Learning, Azure Storage, Azure NetApp Files, Azure Virtual Network, Entra ID, Azure Monitor, and Azure DevOps or GitHub Actions.
- Experience architecting GPU and accelerated compute, distributed training, model serving, high-throughput storage, container platforms, and secure cloud networking.
- Working knowledge of Bicep, ARM, Terraform, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response practices.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and architecture patterns, and managing multiple initiatives.
- Preferred qualifications include Azure Solutions Architect Expert, Azure DevOps Engineer Expert, Azure AI Engineer, or Azure Data Engineer credentials.
- Preferred experience includes regulated industry environments, LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, high-performance storage, low-latency model serving, model optimization, FinOps, and enterprise architecture governance.
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.