19 hours ago
Pune, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize AI/ML infrastructure on Microsoft Azure.
- Write, review, and debug code, scripts, and infrastructure-as-code for Azure AI infrastructure and deployment tooling.
- Configure Azure VMs, Azure Kubernetes Service, Azure Machine Learning compute, Azure Storage, networking, and security services.
- Support AI system, model, and application deployment automation using CI/CD, infrastructure-as-code, containers, Kubernetes, and workflow orchestration tools.
- Deploy and operate AI services, model-serving components, and data pipelines with reliability, security, cost-efficiency, and scalability practices.
- Monitor infrastructure and model-serving health using Azure Monitor, Log Analytics, and related observability tools.
- Troubleshoot issues across compute, storage, networking, containers, and application layers.
- 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
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum five years of overall experience is required.
- At least two years of experience coding, building, monitoring, or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- At least two years of proficiency in 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 for AI workloads.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational monitoring tools.
- Hands-on experience with Azure services including 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.
- Working knowledge of Bicep, ARM, Terraform, Docker, Kubernetes, CI/CD pipelines, and observability practices.
- Ability to optimize infrastructure for performance, reliability, scalability, cost, and security.
- Understanding of MLOps patterns including experiment tracking, model registries, model deployment, monitoring, and rollback.
- Azure certification such as Azure Administrator, Azure Developer, Azure Solutions Architect, Azure AI Engineer, or Azure Data Engineer is preferred.
- Familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, security controls, FinOps, incident management, and production support is preferred.
- Exposure to regulated or compliance-sensitive industry use cases is preferred.
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.