15 hours ago
Chennai, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize AI/ML infrastructure on Microsoft Azure for model development and production workloads.
- Configure and provision Azure VMs, Azure Kubernetes Service, Azure Machine Learning compute, Azure Storage, networking, and security services.
- Develop and maintain automation, infrastructure-as-code, monitoring, deployment tooling, CI/CD pipelines, and workflow orchestration for AI systems.
- Deploy and operate AI services, model-serving components, data pipelines, GPU-accelerated compute environments, and distributed training workloads.
- Monitor infrastructure and model-serving health using Azure Monitor, Log Analytics, and related observability tools, and troubleshoot compute, storage, networking, container, and application issues.
- Collaborate with data scientists, ML engineers, platform engineers, and architects to integrate models into enterprise systems while meeting security, compliance, and operational requirements.
- Document reusable infrastructure patterns, configuration standards, and operational runbooks.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum five years of experience is stated for the role; the qualifications also specify at least two years working with AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- At least two years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Strong understanding of AI/ML concepts and the compute, storage, networking, security, and deployment foundations needed to run AI workloads.
- Hands-on experience with Azure VMs, Azure Kubernetes Service, Azure Machine Learning, Azure Storage, Azure Virtual Network, Azure Monitor, Log Analytics, and Azure DevOps or GitHub Actions.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, operational monitoring, GPU/accelerated compute, distributed training, containerized deployments, and model-serving workloads.
- Working knowledge of Bicep/ARM, Terraform, Docker, Kubernetes, observability practices, and MLOps patterns including experiment tracking, model registries, deployment, monitoring, and rollback.
- Ability to optimize infrastructure for performance, reliability, scalability, cost, and security.
- Preferred qualifications include an Azure Administrator, Azure Developer, Azure Solutions Architect, Azure AI Engineer, or Azure Data Engineer certification; familiarity with LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, security controls, FinOps, incident management, 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.