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Ai Infrastructure Job Jobs in Virginia (NOW HIRING)

The Josef Group

AI Infrastructure Engineer

Chantilly, VA

$110K - $144K/yr

AI Infrastructure Engineer Top Secret or TS/SCI is required to start $200K to $250K Chantilly, VA What You'll Do * Deploy and optimize self-hosted LLM inference servers (vLLM, Ollama, and similar)

The Josef Group

AI Infrastructure Engineer

Chantilly, VA · On-site

$200 - $250/hr

AI Infrastructure Engineer Top Secret or TS/SCI is required to start $200K to $250K Chantilly, VA What You'll Do * Deploy and optimize self-hosted LLM inference servers (vLLM, Ollama, and similar)

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 ...

Venturegloballng

Senior AI Platform Engineer

Arlington, VA

$120K - $165K/yr

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 ...

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Ai Infrastructure Job information

What is an AI infrastructure job?

An AI Infrastructure job involves designing, building, and maintaining the foundational systems and tools required to support artificial intelligence and machine learning workloads. These professionals work on scalable computing environments, manage large datasets, and ensure high-performance computing resources are available for AI development and deployment. Their responsibilities often include optimizing hardware (like GPUs and TPUs), developing cloud-based solutions, and establishing reliable pipelines for data and model management. AI Infrastructure specialists collaborate with data scientists, ML engineers, and IT teams to deliver robust, efficient, and secure platforms that power AI applications.

What are some common challenges faced by professionals working in AI infrastructure roles?

Professionals in AI infrastructure roles often encounter challenges such as ensuring scalability and reliability of systems to handle large volumes of data and compute-intensive workloads. Managing the integration of diverse tools and frameworks, optimizing hardware and cloud resources, and maintaining security and compliance are also critical aspects. Additionally, effective collaboration with data scientists, software engineers, and DevOps teams is essential to streamline AI model deployment and monitoring. Staying current with rapidly evolving technologies is key to overcoming these challenges.

What are the key skills and qualifications needed to thrive in an AI infrastructure role, and why are they important?

To excel in an AI Infrastructure role, you need a strong background in computer science, cloud computing, and distributed systems, often supported by a relevant degree and experience with large-scale data environments. Proficiency with tools like Kubernetes, Docker, TensorFlow, PyTorch, and cloud platforms such as AWS, Azure, or Google Cloud is essential, along with familiarity with CI/CD pipelines and automation frameworks. Strong problem-solving, collaboration, and communication skills help drive innovation and facilitate cross-functional teamwork. These abilities ensure the robust, scalable, and efficient deployment of AI solutions that meet organizational needs.

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

AspectAi Infrastructure JobData Engineer
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of AI frameworksBachelor's in CS, Data Science, or related; programming skills in SQL, Python
Work EnvironmentData centers, cloud platforms, AI labsData pipelines, cloud environments, database systems
Industry UsageAI development, machine learning deploymentData processing, analytics, data pipeline creation

Ai Infrastructure Jobs focus on building and maintaining the hardware and software systems that support AI applications, including cloud and data center infrastructure. Data Engineers primarily develop and manage data pipelines and storage solutions to enable data analysis and machine learning. While both roles require technical skills and work in tech environments, Ai Infrastructure Jobs are more hardware and system-focused, whereas Data Engineers concentrate on data flow and management.

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

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

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

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

Ai Infrastructure Job jobs near you
Infographic showing various Ai Infrastructure Job job openings in Virginia as of August 2026, with employment types broken down into 78% Full Time, 6% Temporary, and 16% Contract. Highlights an 94% In-person, and 6% Hybrid job distribution.

AI Infrastructure Engineer

The Josef Group

Chantilly, VA

$110K - $144K/yr

Full-time

Re-posted 21 days ago

Job description

AI Infrastructure EngineerTop Secret or TS/SCI is required to start 
$200K to $250K 
Chantilly, VAWhat You'll Do
  • Deploy and optimize self-hosted LLM inference servers (vLLM, Ollama, and similar).
  • Containerize AI workloads using Docker and orchestrate production environments with Kubernetes, including GPU scheduling.
  • Build and maintain AI serving infrastructure, including gateways, load balancing, authentication, TLS, and rate limiting.
  • Optimize GPU utilization, memory management, quantization, batching, and capacity planning to balance performance and cost.
  • Develop and maintain CI/CD pipelines, observability, monitoring, and incident response processes.
What You'll Bring (Required)
  • Hands-on experience deploying and serving Large Language Models (LLMs) in production.
  • Strong experience with Docker and production Kubernetes environments, including GPU scheduling.
  • Deep understanding of self-hosted AI infrastructure, including model formats, quantization, GPU memory management, batching, and inference optimization.
  • Experience supporting production applications with networking, reverse proxies, load balancing, authentication, and TLS.
  • Proficiency with Linux administration and Python and/or Bash scripting.
  • Ownership mindset with the ability to operate and improve production AI infrastructure.
Nice to Have
  • Experience with CUDA, NVIDIA drivers, GPU Operators, or other GPU infrastructure technologies.
  • Experience with Infrastructure as Code (Terraform, Helm).
  • Familiarity with observability and monitoring tools such as Prometheus and Grafana.
  • Experience building Retrieval-Augmented Generation (RAG) pipelines and working with vector databases (pgvector, Qdrant, Weaviate).
  • Experience with LLM gateway tools such as LiteLLM.