17 hours ago
Pune, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize AI/ML infrastructure on Microsoft Azure.
- Configure and provision Azure VMs, Azure Kubernetes Service, Azure Machine Learning compute, Azure Storage, networking, and security services.
- Write, review, and debug code, scripts, and infrastructure-as-code for Azure AI infrastructure, automation, monitoring, and deployment tooling.
- Support CI/CD and deployment automation for AI systems, models, and applications using Git, Bicep/ARM, Terraform, Azure DevOps, GitHub Actions, Docker, Kubernetes, and workflow orchestration tools.
- Deploy and operate AI services, model-serving components, and data pipelines with attention to reliability, security, scalability, and cost efficiency.
- Monitor infrastructure and model-serving health with Azure Monitor, Log Analytics, and related observability tools, troubleshooting compute, storage, networking, container, and application issues.
- Collaborate with data scientists, ML engineers, platform engineers, and architects to integrate AI models into enterprise systems.
- Document reusable infrastructure patterns, configuration standards, and operational runbooks.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum 5 years of experience is required; the qualifications also specify at least 2 years working with AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- At least 2 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.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational monitoring tools.
- Hands-on experience with Azure VMs, AKS, Azure Machine Learning, Azure Storage, Azure Virtual Network, Azure Monitor, Log Analytics, and Azure DevOps or GitHub Actions.
- Experience designing or operating GPU/accelerated compute, distributed training environments, containerized deployments, and model-serving workloads.
- Understanding of MLOps practices including experiment tracking, model registries, deployment, monitoring, and rollback.
- Preferred qualifications include 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.