16 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Own end-to-end architecture and design of optimized Azure compute infrastructure for large-scale AI/ML systems.
- Design and tune Azure GPU clusters, distributed training systems, container platforms, model-serving environments, networking, and high-throughput storage.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, observability, security, compliance, cost, and operational complexity.
- Lead architecture assessments and reviews, identify risks and bottlenecks, and recommend remediation actions.
- Define roadmap inputs, 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 monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, and performance and cost tracking.
- Integrate AI/ML systems into enterprise environments while maintaining interoperability, security, compliance, and regulatory alignment.
- Collaborate with clients, architects, stakeholders, and engineering teams to translate business requirements into infrastructure standards.
- Set technical direction, mentor engineers, review designs and code, and promote engineering best practices.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum 12 years of experience is required for the role.
- At least 4 years of experience 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 programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell.
- At least 4 years of AI/ML infrastructure engineering or related experience on a hyperscaler or enterprise platform.
- 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 with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Experience architecting GPU or 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 producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
- Experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
- Preferred qualifications include Azure Solutions Architect Expert, Azure DevOps Engineer Expert, Azure AI Engineer, or Azure Data Engineer certifications.
- Preferred experience includes regulated-industry AI infrastructure, LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, high-performance storage, low-latency model serving, model optimization, FinOps, and enterprise architecture governance.
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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.