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Ai Infrastructure Jobs (NOW HIRING)

AMAX

AI Infrastructure Engineer

Fremont, CA · On-site

$117K - $154K/yr

* Design, build, and operate on-prem infrastructure that behaves like a cloud environment for internal teams, including AI/ML workloads * Own datacenter and infrastructure operations: compute, storage ...

AMAX

AI Infrastructure Engineer

Fremont, CA · On-site

$126K - $165K/yr

* Design, build, and operate on-prem infrastructure that behaves like a cloud environment for internal teams, including AI/ML workloads * Own datacenter and infrastructure operations: compute, storage ...

Seekr

Manager, AI Infrastructure

Austin, TX · On-site

We are seeking an Infrastructure Manager with deep expertise in Kubernetes, Terraform, and Ansible to help scale Seekr's AI platform across on-premises, cloud, and SaaS environments. You'll be highly ...

Scout Motors

AI Infrastructure Engineer

Charlotte, NC

$140K - $170K/yr

Install, configure, and maintain AI platform and tools. * Design, deploy, and operate infrastructure supporting ML and LLM workloads. * Build, containerize, and deploy AI services using Docker.

AMAX

AI Infrastructure Engineer

Fremont, CA · On-site

$126K - $165K/yr

* Design, build, and operate on-prem infrastructure that behaves like a cloud environment for internal teams, including AI/ML workloads * Own datacenter and infrastructure operations: compute, storage ...

Spellbrush

AI Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

Spellbrush is the world's leading generative AI studio behind niji・journey, and they are seeking an AI Infrastructure Engineer to build out end-to-end ML infrastructure. The role involves designing ...

Accenture

AI & HPC Infrastructure Engineer

Saint Louis, MO · On-site

$97K - $127K/yr

The Global AI Infrastructure team is at the center of enabling infrastructure reinvention for the next era of digital solutions powered by AI, accelerated computing, and high-performance workloads.

Bitdeer Group

WI · On-site

$100 - $130/hr

About the Role Architecting, deploying, and maintaining the infrastructure required for large-scale AI compute environments in a high-density AI data center. What You Will Be Responsible For * Deploy ...

Scout Motors

AI Infrastructure Engineer

Charlotte, NC · On-site

$105K - $137K/yr

Install, configure, and maintain AI platform and tools. * Design, deploy, and operate infrastructure supporting ML and LLM workloads. * Build, containerize, and deploy AI services using Docker.

Anaconda

Alliances Director- AI Infrastructure

Learn more at Summary: We're looking for an Alliances Director- AI Infrastructure anchored by NVIDIA, our most important AI partner. You'll build and run the relationship end to end, driving ...

Accenture

AI Infrastructure Operations Engineer

Carmel, NY · Hybrid

$108K - $142K/yr

The Global AI Infrastructure team enables resilient, high-performance compute environments for strategic clients across cloud, on-premises, and hybrid deployments. We design, build, and operate large ...

Accenture

AI Infrastructure Operations Engineer

Miami, FL · Hybrid

$102K - $134K/yr

The Global AI Infrastructure team enables resilient, high-performance compute environments for strategic clients across cloud, on-premises, and hybrid deployments. We design, build, and operate large ...

Accenture

AI Infrastructure Operations Engineer

Raleigh, NC · Hybrid

$104K - $137K/yr

The Global AI Infrastructure team enables resilient, high-performance compute environments for strategic clients across cloud, on-premises, and hybrid deployments. We design, build, and operate large ...

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Ai Infrastructure information

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How much do ai infrastructure jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for ai infrastructure in the United States is $59.18, according to ZipRecruiter salary data. Most workers in this role earn between $48.08 and $68.99 per hour, depending on experience, location, and employer.

What is AI infrastructure?

AI infrastructure refers to the combination of hardware, software, and cloud-based solutions that support the development, deployment, and scaling of artificial intelligence applications. It includes components such as GPUs, CPUs, storage systems, networking, data management tools, and machine learning frameworks. The goal of AI infrastructure is to provide the computational power and resources needed to train, test, and run AI models efficiently, whether on-premises or in the cloud. Organizations invest in robust AI infrastructure to accelerate innovation, manage large datasets, and ensure the reliability of their AI systems.

What are the key skills and qualifications needed to thrive in AI infrastructure?

To thrive in AI Infrastructure, you need expertise in software engineering, distributed systems, cloud platforms, and a solid understanding of machine learning workflows, often supported by degrees in computer science or related fields. Familiarity with tools like Kubernetes, Docker, Terraform, and cloud services (AWS, GCP, Azure), as well as experience with CI/CD pipelines and monitoring systems, is essential. Strong problem-solving abilities, effective communication, and adaptability help professionals excel in cross-functional teams and rapidly evolving environments. These skills and qualities are crucial for building scalable, reliable systems that power AI applications and support organizational innovation.

What are common challenges faced by professionals working in AI infrastructure roles, and how can they be addressed?

Professionals in AI Infrastructure roles often encounter challenges related to scalability, system reliability, and integration with existing IT environments. Managing rapidly growing datasets and ensuring seamless deployment of machine learning models can be complex, requiring robust automation and monitoring tools. Collaboration with data scientists, software engineers, and DevOps teams is critical to ensure infrastructure meets the evolving needs of AI projects. Staying updated with the latest cloud technologies and best practices can help address these challenges and drive successful AI implementations.

What is the difference between Ai Infrastructure vs Data Engineer?

AspectAi InfrastructureData Engineer
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of cloud platforms and AI toolsBachelor's in CS, Data Science, or related; programming and database skills
Work EnvironmentCloud environments, AI model deployment, infrastructure setupData pipelines, database management, data processing
Employer & Industry UsageTech companies, AI startups, cloud providersTech firms, finance, healthcare, e-commerce

Ai Infrastructure professionals focus on building and maintaining the hardware and software systems that support AI models, while Data Engineers develop and manage data pipelines and databases. Both roles require technical skills and often collaborate but serve different core functions within AI and data ecosystems.

What are AI infrastructure jobs?

AI infrastructure jobs involve designing, building, and maintaining the hardware, software, and network systems necessary to support artificial intelligence applications. These roles often require knowledge of cloud computing, data centers, machine learning frameworks, and system optimization to ensure reliable and efficient AI model deployment and operation.
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Ai Infrastructure jobs near you
Infographic showing various Ai Infrastructure job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $123,103 per year, or $59.2 per hour.

Lead AI Infrastructure Engineer

Staffed4U

Annapolis Junction, MD

$293K - $306K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago

Key responsibilities

  • Design, implement, and optimize infrastructure supporting large-scale AI model deployment and inference services.

  • Lead the development, deployment, and maintenance of production AI applications and platform services.

  • Serve as the technical lead for AI infrastructure initiatives, coordinating activities across engineering teams and stakeholders.

Job description

Lead AI Infrastructure Engineer

Location: Annapolis Junction, MD
Clearance: TS/SCI with Polygraph required
Work Type: On-site
Salary: $293,000-$306,000

Position Overview

We are seeking an experienced Lead AI Infrastructure Engineer to provide technical leadership for the design, deployment, and operation of enterprise artificial intelligence and machine learning platforms. This role will lead the development and sustainment of critical AI infrastructure components, with a focus on scalable model deployment, platform reliability, and support for AI-enabled applications and services.

The successful candidate will combine hands-on engineering expertise with team leadership responsibilities, serving as a technical lead for platform initiatives while supporting the professional development of engineering staff. This position requires strong cloud engineering, platform architecture, and organizational leadership skills to drive innovation, operational excellence, and technology adoption across multiple teams.

Key Responsibilities
  • Design, implement, and optimize infrastructure supporting large-scale AI model deployment and inference services.
  • Lead the development, deployment, and maintenance of production AI applications and platform services.
  • Serve as the technical lead for AI infrastructure initiatives, coordinating activities across engineering teams and stakeholders.
  • Provide mentorship, coaching, and professional development support to engineering team members.
  • Support team operations, resource planning, and administrative coordination activities.
  • Define technical solutions for complex and evolving requirements.
  • Establish and maintain technical standards, policies, governance processes, and engineering best practices.
  • Drive adoption of emerging technologies, automation capabilities, and platform modernization initiatives.
  • Design, implement, and oversee monitoring, logging, alerting, and observability solutions.
  • Ensure the reliability, availability, scalability, performance, and security of AI platform components.
  • Communicate technical strategies, project status, and recommendations to stakeholders at multiple organizational levels.
  • Lead troubleshooting, root cause analysis, and continuous improvement efforts for production systems.
Required QualificationsEducation and Experience
  • Bachelor's degree in Computer Science, Software Engineering, Information Systems, Computer Engineering, or a related technical discipline and twelve (12) years of relevant experience; OR
  • Four (4) additional years of directly related experience may be substituted for the degree requirement.
Technical Qualifications
  • Extensive experience designing, building, deploying, and operating enterprise-scale production systems.
  • Deep expertise in systems integration across diverse technologies, platforms, and cloud environments.
  • Hands-on experience designing, deploying, and managing cloud infrastructure within Amazon Web Services (AWS).
  • Advanced experience administering and deploying applications using Kubernetes.
  • Strong software development skills using Python.
  • Experience implementing and scaling observability solutions using technologies such as:
    • Application Performance Monitoring (APM) tools
    • OpenTelemetry
    • Grafana
    • Prometheus
  • Experience developing and maintaining highly available, resilient, and secure distributed systems.
  • Proven ability to lead complex technical initiatives and influence organizational technology adoption.
  • Experience establishing technical standards, governance frameworks, and engineering policies.
  • Excellent communication, stakeholder engagement, and leadership skills.
  • Demonstrated ability to balance hands-on engineering responsibilities with leadership and team coordination duties.
Preferred Qualifications
  • Experience supporting AI model serving and inference platforms.
  • Experience integrating large language models (LLMs) and generative AI technologies into enterprise applications.
  • Experience with AI orchestration and workflow frameworks, including LangChain or similar technologies.
  • Knowledge of vector databases, embeddings, and semantic search technologies.
  • Experience implementing Retrieval-Augmented Generation (RAG) architectures.
  • Experience with distributed computing, high-performance computing, or large-scale processing environments.
  • Demonstrated success leading technical transformation, modernization, or organizational change initiatives.
  • Familiarity with autonomous agent frameworks and emerging AI technologies.
Knowledge, Skills, and Abilities
  • Strong leadership and technical decision-making capabilities.
  • Expertise in cloud-native architecture, platform engineering, and distributed systems.
  • Ability to balance reliability, scalability, security, and performance requirements.
  • Strong analytical and problem-solving skills.
  • Ability to establish technical direction and influence engineering organizations.
  • Excellent written and verbal communication skills.
  • Strong mentoring, coaching, and team development abilities.
  • Ability to work effectively across technical and non-technical stakeholder groups.
  • Strong organizational skills and attention to detail.
Benefits

This position includes a competitive and flexible benefits package, including:

  • Medical
    Employer pays 100% of the monthly premium for the employee and 80% for the employee’s dependents.

  • Health Savings Account (HSA)
    Save for all medical, dental, vision and prescription expenses by contributing pre-tax money to an HSA account. Employer contributes 50% of the annual deductible (prorated to start date).

  • Dental and Vision
    Employer pays 100% of the monthly premium for the employee and 80% for dependents.

  • Life Insurance
    100% company-paid Life and Accidental Death & Dismemberment (AD&D) coverage offered to all full-time employees.

  • Short-Term Disability
    100% company-paid short-term disability. This benefit pays out 60% of earnings, with a $1,500 maximum for up to 12 weeks.

  • Retirement Plan
    Automatic 6% of salary contributed to the company 401(k) plan, fully vested. Employee match encouraged but not required.

  • Paid Time Off (PTO) & Holidays
    5–6 weeks of PTO based on tenure with the company, in addition to 11 paid holidays.

  • Tuition Reimbursement
    $5,000 annually for courses directly related to job role and responsibilities.

  • Training Reimbursement
    Paid training, certification courses, and conferences to support employee career growth.

We do not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factor.