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Internship Ai Infrastructure Engineer Jobs in Virginia

Venture Global, Inc.

Senior AI Platform Engineer

Arlington, VA · On-site

$120K - $165K/yr

Arlington Summary The Senior AI Platform Engineer is responsible for building and operating the infrastructure that powers Venture Global's AI Engineering function. This role designs, stands up, and ...

Deloitte

AI Datacenter Associate VP

Mclean, VA · Hybrid

AI Datacenter & Infrastructure Associate VP Join our AI & Engineering team and help transform technology platforms, drive innovation, and make a significant impact on our clients' success. You'll ...

LMI

Advanced Engineering and Infrastructure Lead

Tysons, VA · On-site

$108K - $142K/yr

Overview LMI is looking for an Advanced Engineering and Infrastructure Lead to design, build, and ... seamlessly with enterprise AI systems, business applications, IT service delivery, and ...

LMI

Advanced Engineering and Infrastructure Lead

Tysons, VA · Hybrid

$108K - $142K/yr

LMI is looking for an Advanced Engineering and Infrastructure Lead to design, build, and operate ... seamlessly with enterprise AI systems, business applications, IT service delivery, and ...

LMI

Advanced Engineering and Infrastructure Lead

Tysons, VA · Hybrid

$108K - $142K/yr

Overview LMI is looking for an Advanced Engineering and Infrastructure Lead to design, build, and ... seamlessly with enterprise AI systems, business applications, IT service delivery, and ...

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All JobsAi Infrastructure Engineer JobsInternship Ai Infrastructure Engineer JobsInternship Ai Infrastructure Engineer Jobs in Virginia

Internship Ai Infrastructure Engineer information

What does an internship AI infrastructure engineer do?

An Internship AI Infrastructure Engineer assists in designing, developing, and maintaining the foundational systems that support artificial intelligence (AI) applications. They work with cloud platforms, data pipelines, and scalable computing resources to ensure that AI models can be trained and deployed efficiently. Interns may help automate workflows, optimize performance, and collaborate with data scientists and software engineers. The role provides hands-on experience with the tools and frameworks commonly used in AI engineering environments.

What types of projects and responsibilities can an internship AI infrastructure engineer expect to work on?

As an AI Infrastructure Engineer intern, you can expect to be involved in projects that support the development, deployment, and scaling of AI models. Typical responsibilities may include optimizing data pipelines, maintaining and improving cloud or on-premise computing resources, and collaborating closely with data scientists to ensure efficient model training and inference. Interns often get hands-on experience with tools such as Docker, Kubernetes, and various cloud platforms, and work in cross-functional teams to troubleshoot and enhance AI workflows. This role provides a solid foundation in both software engineering and AI operations, preparing you for advanced positions in the field.

What are the key skills and qualifications needed to thrive as an internship AI infrastructure engineer, and why are they important?

To thrive as an Internship AI Infrastructure Engineer, you need a solid understanding of computer science fundamentals, programming (especially in Python or C++), and basic knowledge of machine learning frameworks, often supported by ongoing studies in a relevant field. Familiarity with cloud platforms (like AWS, GCP, or Azure), version control systems (such as Git), and containerization tools (Docker, Kubernetes) is typically expected. Strong problem-solving abilities, curiosity, teamwork, and effective communication help interns stand out and integrate quickly into engineering teams. These skills are crucial for supporting scalable AI solutions, collaborating on complex projects, and contributing meaningfully in a fast-evolving technical environment.

What is the difference between Internship Ai Infrastructure Engineer vs Data Engineer?

AspectInternship Ai Infrastructure EngineerData Engineer
Required CredentialsEnrolled in or recent graduate of Computer Science, Engineering, or related fields; some knowledge of AI and infrastructure toolsBachelor's or higher in Computer Science, Data Science, or related; experience with databases and data pipelines
Work EnvironmentInternship setting, collaborative teams, learning-focusedFull-time, technical teams managing data systems and pipelines
Employer & Industry UsageTech companies, AI startups, research labsTech firms, finance, healthcare, and other data-driven industries

The Internship Ai Infrastructure Engineer role focuses on supporting AI infrastructure projects during an internship, emphasizing learning and assisting with AI systems setup. In contrast, Data Engineers build and maintain data pipelines and infrastructure for data analysis. While both roles require knowledge of technical tools, the internship role is more entry-level and learning-oriented, whereas Data Engineers are more experienced and responsible for ongoing data management.

What are the most commonly searched types of Ai Infrastructure Engineer jobs in Virginia?

The most popular types of Ai Infrastructure Engineer jobs in Virginia are:

What are popular job titles related to Internship Ai Infrastructure Engineer jobs in Virginia?

For Internship Ai Infrastructure Engineer jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Internship Ai Infrastructure Engineer jobs in Virginia look for?

The top searched job categories for Internship Ai Infrastructure Engineer jobs in Virginia are:

What cities in Virginia are hiring for Internship Ai Infrastructure Engineer jobs?

Cities in Virginia with the most Internship Ai Infrastructure Engineer job openings:

Internship Ai Infrastructure Engineer jobs near you
Infographic showing various Internship Ai Infrastructure Engineer job openings in Virginia as of August 2026, with employment types broken down into 71% Full Time, 27% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution.

Senior AI Platform Engineer

Venture Global, Inc.

Arlington, VA • On-site

$120K - $165K/yr

Full-time

Posted 7 days ago

Job description

Venture Global LNG ("Venture Global") is a long-term, low-cost provider of American-produced liquefied natural gas. The company's Louisiana-based export projects service the global demand for North American natural gas and support the long-term development of clean and reliable North American energy supplies. Using reliable, proven technology in an innovative plant design configuration, Venture Global's modular, mid-scale plant design will replace traditional designs as it allows for the same efficiency and operational reliability at significantly lower capital cost.
We are seeking qualified applicants for the position:
Senior AI Platform Engineer
Located:
Arlington
Summary
The Senior AI Platform Engineer is responsible for building and operating the infrastructure that powers Venture Global's AI Engineering function. This role designs, stands up, and maintains the compute, serving, and tooling foundation for AI agents and machine learning systems across both cloud and secure on-premises environments, with on-premises infrastructure driven by data residency and security requirements. The Senior AI Platform Engineer owns the deployment and optimization of self-hosted open-weight large language models, GPU infrastructure, and the platform services that enable engineers to build and ship reliably and securely. This role works closely with the Principal AI Engineer on architecture, with Data Engineering on data and platform integration, and with IT and security teams to ensure a hardened, well-governed environment. The measures of an ideal candidate include infrastructure expertise, operational rigor, automation mindset, security awareness, collaboration, and a strong bias for reliability. This new position will be based in our Arlington, VA headquarters and report to the Director of AI Engineering.
The position is full-time in office located in Arlington, VA.
General Description Duties & Responsibilities
  • Design, build, and maintain AI infrastructure across cloud and on-premises environments, including GPU compute clusters.
  • Deploy, serve, and optimize self-hosted open-weight LLMs, applying techniques such as quantization, batching, and inference optimization to maximize throughput and minimize latency and cost.
  • Build and operate the platform services, pipelines, and tooling that enable AI engineers to develop, test, and deploy agents and models reliably.
  • Implement scalable serving infrastructure supporting both batch and streaming inference workloads.
  • Establish and maintain MLOps/LLMOps capabilities, including model registries, CI/CD for models and agents, monitoring, observability, and automated deployment.
  • Support procurement of GPU servers and AI infrastructure by defining specifications, benchmarking hardware, and validating capacity plans.
  • Harden the AI platform in partnership with IT and security teams, implementing appropriate segmentation, access controls, and governance.
  • Integrate AI platform infrastructure with enterprise data systems, including Databricks and streaming platforms.
  • Automate infrastructure provisioning and configuration using infrastructure-as-code and modern DevOps practices.
  • Monitor platform performance, reliability, and utilization; troubleshoot and resolve infrastructure and serving issues.
  • Contribute to architecture and design decisions alongside the Principal AI Engineer and Director of AI Engineering.
  • Document platform architecture, runbooks, and operational procedures for maintainability.

Qualifications
  • 7+ years of experience in infrastructure, platform, DevOps, or machine learning engineering.
  • Bachelor's degree in Computer Science, Engineering, Mathematics, or related field of study.
  • Hands-on experience deploying and operating GPU infrastructure for AI/ML workloads, on-premises and/or in the cloud.
  • Experience serving and optimizing large language models, including familiarity with inference/serving frameworks and optimization techniques (e.g., quantization, batching).
  • Strong proficiency with cloud platforms and containerization/orchestration technologies.
  • Proficiency in Python and infrastructure-as-code tooling.
  • Experience building CI/CD pipelines and implementing MLOps/LLMOps practices.
  • Solid understanding of security, networking, and access control in enterprise environments.
  • Strong troubleshooting skills and operational discipline for maintaining reliable production systems.
  • Excellent interpersonal and communication skills, with strong critical thinking and attention to detail.
  • Strong work ethic with the ability to effectively prioritize, meet deadlines, adapt to changing priorities, and succeed in a fast-paced environment.

Preferred Experience
  • Experience with LLM serving frameworks such as vLLM, TGI, TensorRT-LLM, or similar.
  • Experience with Databricks and streaming data platforms such as Kafka.
  • Experience with on-premises AI infrastructure in environments with data residency or security constraints.
  • Experience deploying infrastructure in or adjacent to operational technology (OT), industrial, or safety-critical environments.
  • Familiarity with agent frameworks and their deployment/runtime requirements.
  • Experience with GPU cluster management, scheduling, and utilization optimization.
  • Experience utilizing DevOps/MLOps tooling and observability stacks.
  • Strong technical writing skills.

Salary Range
$170,240 - $212,800
Venture Global LNG is an Equal Opportunity Employer. We do not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law.

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