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Ai Infrastructure Jobs in Silver Spring, MD (NOW HIRING)

The Josef Group

AI Infrastructure Engineer

Chantilly, VA · On-site

$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)

BigBear.ai

Software Engineer (AI Infrastructure)

Columbia, MD · On-site

$170K - $201K/yr

Overview BigBear.ai is seeking a S oftware Engineer to support our AI infrastructure team. In this role, you'll help build and maintain the platform that provides the foundation for the customer's AI ...

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How much do ai infrastructure jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for ai infrastructure in Silver Spring, MD is $61.18, according to ZipRecruiter salary data. Most workers in this role earn between $49.71 and $71.30 per hour, depending on experience, location, and employer.

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.

How much do AI infrastructure engineers make?

AI infrastructure engineers typically earn between $100,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and hardware may earn higher salaries, often exceeding $180,000.

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.

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 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 job categories do people searching Ai Infrastructure jobs in Silver Spring, MD look for? The top searched job categories for Ai Infrastructure jobs in Silver Spring, MD are:
What cities near Silver Spring, MD are hiring for Ai Infrastructure jobs? Cities near Silver Spring, MD with the most Ai Infrastructure job openings:
Ai Infrastructure jobs near you
Infographic showing various Ai Infrastructure job openings in Silver Spring, MD as of August 2026, with employment types broken down into 14% Internship, 72% Full Time, and 14% Part Time. Highlights an 79% In-person, 7% Hybrid, and 14% Remote job distribution, with an average salary of $127,261 per year, or $61.2 per hour.

AI Infrastructure Engineer

The Josef Group

Chantilly, VA • On-site

$110K - $144K/yr

Full-time

Re-posted 6 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.