3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Own end-to-end architecture and design of optimized Azure compute infrastructure for large-scale AI and machine learning systems.
- Design and tune Azure GPU clusters, distributed training systems, model-serving environments, container platforms, networking, and high-throughput storage.
- Evaluate architecture alternatives and lead assessments, reviews, remediation planning, and architecture decision documentation.
- Define infrastructure roadmaps, capacity plans, scaling strategies, cost forecasts, performance improvements, and operational controls.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices covering SLAs, SLOs, alerting, performance, cost tracking, and continuous optimization.
- Integrate AI/ML systems into enterprise environments while addressing interoperability, security, compliance, regulatory requirements, and client standards.
- Collaborate with clients and technical stakeholders to align infrastructure decisions with business outcomes.
- Set technical direction, lead engineering workstreams, review designs and code, and mentor engineers.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- At least 7.5 years of experience is required; the posting also specifies experience building, operating, and designing AI/ML infrastructure, cloud or 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, PowerShell, or equivalent engineering languages.
- Hands-on experience with Azure AI infrastructure services, GPU or accelerated computing, distributed training, model serving, high-throughput storage, container platforms, and secure cloud networking.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Strong working knowledge of Bicep, ARM, Terraform, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response practices.
- Experience optimizing AI infrastructure for performance, power, cost, scalability, security, reliability, and compliance.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
- Demonstrated ability to evaluate and select 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; industry experience with regulated workloads; and exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, high-performance storage, low-latency model serving, model optimization, FinOps, and enterprise architecture governance.
- Excellent communication, collaboration, stakeholder alignment, problem-solving, and client delivery skills.
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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.