Artificial Intelligence & Engineering
AI & Engineering leverages cutting-edge engineering capabilities to help build, deploy, and operate integrated sector solutions across software, data, artificial intelligence, networking, and hybrid cloud infrastructure. These engineering-led solutions help transform mission-critical operations and deliver business value.
Join our AI & Engineering team to help transform technology platforms, drive innovation, and make a meaningful impact on client outcomes. You will work alongside professionals who are reimagining operations and engineering solutions that are critical to business performance.
Level: Lead Cloud Integrated Infrastructure Engineer II
As an AI Infra Architect in Deloitte’s AI&E practice, you will design and drive deployment of fully integrated architecture for GPU-accelerated AI factories and high-performance computing infrastructure in close partnership with Deloitte AI specialists and our ecosystem partners. You will shape end-to-end solutions – from discovery and reference architecture mapping through sizing and implementation. You will be a partner with Sales Executives, AI application specialists, delivery engineering, and managed services to help clients achieve measurable outcomes from private AI assets. You will lead technical solution strategy for pursuits and active opportunities and translate complex client needs into clear, complete solutions and delivery requirements. Your role spans the full project lifecycle, including estimation, planning, execution, and tracking key metrics for analysis, ensuring high-quality and timely delivery of solutions.
Work you’ll do:
As a Cloud Integrated Infrastructure Engineer II on the Hybrid Cloud Infrastructure team, you will be responsible for…
- Leading reference architecture design across compute, network, storage, platform, and software layers for artificial intelligence and high-performance computing environments spanning edge, data center, and hybrid deployments.
- Translating business strategy into scalable, secure, and cost-informed infrastructure solutions, including deployment strategies for NVIDIA platforms, GPU clusters, and enterprise artificial intelligence and machine learning environments.
- Architecting and guiding implementation of containerized, automated, and policy-driven platforms using technologies such as Kubernetes, Slurm, Run:ai, Terraform, Ansible, and GitOps.
- Defining infrastructure standards for observability, site reliability engineering, resilience, high availability, security, and compliance across mission-critical artificial intelligence and high-performance computing workloads.
- Leading technical delivery, escalation support, stakeholder engagement, and team mentorship across pursuits, active implementations, and ongoing transformation programs.
The team
Within Hybrid Cloud Infrastructure, Deloitte delivers solutions across hybrid cloud, advanced connectivity, artificial intelligence data centers, high-performance computing, and artificial intelligence infrastructure to help clients achieve business outcomes. The practice provides engineered transformation services focused on resiliency, optimization, automation, and operational performance across infrastructure platforms and field environments. The team brings together infrastructure, connectivity, and enterprise network capabilities to enable real-time data processing and support complex transformation programs.
Location: Hyderabad/Bengaluru
Shift Timings: As per Business Requirement
Qualifications
Required:
- 8-12+ years of experience in infrastructure architecture or engineering for large-scale platforms, including design, implementation, operations, and optimization.
- Experience designing or delivering GPU-accelerated platforms for artificial intelligence, machine learning, or high-performance computing workloads.
- Experience with Linux system administration in production environments.
- Experience designing or operating distributed compute clusters for artificial intelligence or high-performance computing in hybrid cloud environments, including multi-GPU topologies, scheduler integration, and edge-to-cloud scalability.
- Experience building containerized platforms using Kubernetes or Red Hat OpenShift, including GPU operators, drivers, CUDA container runtime, and cluster lifecycle automation.
- Experience automating infrastructure as code using Terraform, Ansible, or both.
- Bachelor of Engineering, Bachelor of Technology, Master of Computer Applications, Master of Science in Computer Science, Master of Technology, or equivalent from an accredited university.
Preferred:
- Experience implementing artificial intelligence or high-performance computing cluster scheduling using Slurm, Kubernetes, or both, including multi-tenant queues, quotas, and GPU-aware policies.
- Experience managing generative artificial intelligence infrastructure patterns, including multi-node distributed training.
- Experience with artificial intelligence agents and artificial intelligence frameworks.
- Experience with high-throughput storage architectures for artificial intelligence or high-performance computing workloads.
- Experience supporting pre-sales or sales engineering activities, including discovery, solution demonstrations, proposals, or request for proposal responses.
- Experience executing NVIDIA co-sell motions with original equipment manufacturers, cloud service providers, or independent software vendors.
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