Consultative Offerings - Analyst - Infrastructure Engineering

Position Summary

Are you ready to build and engineer the backbone of tomorrow's digital enterprise? At Deloitte, we empower you to deliver impactful infrastructure solutions that go far beyond keeping the lights on. Here, you'll turn your curiosity and technical skill into real-world systems that power business growth, innovation, and security. Join us to unlock your potential-and engineer what's next. Reach your exponential!

Recruiting for this role ends on 12/1/2026.

Work You'll Do

As an Infrastructure Engineering Analyst at Deloitte, you'll launch your consulting journey with immersive training across the pillars of modern IT infrastructure. From designing, building secure networks and modern data centers to enabling the adoption of cloud, AI infrastructure, and high-performance computing (HPC), you'll gain hands-on experience with the diverse technology foundations essential for digital transformation.

You'll be exposed to a breadth of environments- supporting traditional IT, hybrid cloud, and specialized GPU-accelerated infrastructure powering critical business processes, advanced analytics, or Physical AI environments (spatial computing, digital twins, edge environments). Whether it's optimizing system performance, deploying automation for scale, fortifying cybersecurity for AI agents and non-human identities, or setting high-performance compute (HPC) clusters for AI workloads, every project will be a new learning opportunity.

With the backing of industry leaders and a culture of mentorship, you'll be set on a career path that can evolve toward AI infrastructure engineer, cloud adoption specialist, network architect, or automation engineer-tailored to your ambitions and Deloitte's commitment to ongoing learning.

Key Responsibilities:

• Advise and Support Clients: Guide organizations through AI infrastructure and network transformation journeys, such as deploying AI Factories, edge computing solutions, modernizing legacy environments, or migrating workloads to the cloud.

• Design and Deliver Solutions: Architect and implement comprehensive infrastructure solutions-including AI platforms, compute resources, storage, and networking-across cloud, on-premises, and hybrid environments.

• Automate and Optimize: Implement automation for infrastructure deployment, management, and monitoring using industry-standard tools (such as Terraform, Ansible, or CloudFormation), including cost visibility and FinOps practices for GPU and AI workload spend driving efficiency and consistency.

• Collaborate Across Teams: Work alongside multi-disciplinary teams-including AI architects, data scientists, and application developers-to deliver optimized infrastructure for AI workloads, business-critical applications, and HPC projects.

• Stay Ahead of Trends: Maintain up-to-date knowledge of advancements in modern data centers, high performance computing, network technologies, AIOps and self-healing systems, ensuring innovative solutions for clients.

• Must be currently enrolled in an accredited college or university and expected to graduate by Spring/Summer 2027 or have completed a bachelor's degree or higher in a STEM-related discipline

• Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future

• Ability to travel up to 50%, on average, based on the work you do and the clients and industries/sectors you serve

• Candidates must be at least 18 years of age at time of employment

• You must reside within a commutable distance of your assigned office with the ability to commute daily, if required

• Foundational knowledge of IT infrastructure domains: networking, operating systems, virtualization, cloud, IT security, and an interest in learning about AI infrastructure and/or high-performance computing concepts

• Familiarity with scripting or automation tools (e.g., Python, PowerShell, Bash)

• Hands-on exposure to building or deploying AI agents and LLM-powered applications-such as working with agentic frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI), Model Context Protocol (MCP) integrations, or multi-agent orchestration-along with familiarity with prompt engineering, retrieval-augmented generation (RAG) pipelines, or fine-tuning workflows for real-world use cases

• Experience or exposure to configuration/monitoring tools (e.g., VMware, AWS/Azure/GCP consoles, or network management tools)