3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Own end-to-end architecture and design of Azure compute infrastructure for large-scale AI/ML systems, including distributed training, GPU-accelerated compute, container platforms, and model-serving environments.
- Design and tune Azure GPU clusters, networking, high-throughput storage, orchestration, and AI/ML platform services.
- Evaluate architecture alternatives and lead assessments, reviews, remediation planning, and technical decision-making.
- Define infrastructure roadmaps, capacity plans, scaling strategies, cost forecasts, performance improvements, and operational standards.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across infrastructure and MLOps, including SLAs, SLOs, alerting, and performance and cost tracking.
- Integrate AI/ML systems into enterprise environments while addressing interoperability, security, compliance, regulatory alignment, and client standards.
- Collaborate with clients, architects, stakeholders, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, lead workstreams, review designs and code, and mentor engineers.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- A minimum of 12 years of experience is stated for the project role, with at least 4 years in AI/ML infrastructure engineering or related large-scale engineering work on a hyperscaler or enterprise platform.
- 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 in programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Strong understanding of AI/ML concepts and the infrastructure required 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 AI infrastructure services, GPU and distributed training architecture, 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.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Experience leading AI projects or engineering workstreams, managing priorities, evaluating technologies and architecture patterns, and aligning stakeholders.
- Preferred qualifications include Azure Solutions Architect Expert, Azure DevOps Engineer Expert, Azure AI Engineer, or Azure Data Engineer certifications.
- 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 vendor collaboration.
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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.