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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)

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)

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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Ai Infrastructure information

See Silver Spring, MD salary details

$29

$61

$90

How much do ai infrastructure jobs pay per hour?

As of Sep 2, 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 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.

What are popular job titles related to Ai Infrastructure jobs in Silver Spring, MD?

For Ai Infrastructure jobs in Silver Spring, MD, the most frequently searched job titles are:

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 78% Full Time, 18% Part Time, and 4% Contract. Highlights an 71% Physical, 4% Hybrid, and 25% Remote job distribution, with an average salary of $127,261 per year, or $61.2 per hour.

AI Infrastructure Lead

Accenture Federal Services

Washington, DC • Hybrid

Full-time

Posted 6 days ago

Accenture Federal Services rating

8.7

Company rating: 8.7 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

51st of 500 rated business services

Job description

You Are:

The AI Infrastructure Lead is a senior technical strategist and handson engineering leader responsible for designing, securing, modernizing, and operating the advanced AI infrastructure environments required to power missioncritical Defense capabilities. This role drives infrastructure strategy, accelerates modernization across hybrid cloud and highperformance computing (HPC) environments, and ensures secure, compliant, endtoend orchestration of AI workloads from onboarding tools and datasets to operating compute clusters to deploying models into classified inference environments.

This individual will shape and lead growth across major AI infrastructure opportunities including SUNet 2.0 modernization, HITSU, and JTSO JEDAI while supporting delivery excellence once pursuits are won. Their work directly enables the creation of modern digital cores, hybrid cloud ecosystems, highassurance AI pipelines, and enterprisescale secure environments aligned with AFS cloud modernization patterns and DoD AIfirst mission priorities.

Key Responsibilities

  1. Lead AI Infrastructure Strategy for Major Defense Modernization Efforts

Drive infrastructure architecture and modernization strategy across:

  • SUNet 2.0 - enabling secure, resilient, hybrid environments connecting cloud and onprem HPC resources.
  • HITSU - supporting classified mission data flows, compute orchestration, and modernized digital core environments.
  • JTSO JEDAI - shaping the infrastructure backbone for Joint AI enablement, crossdomain access, and rapid model deployment.
  1. Drive AI Infrastructure Growth & Pursuit Strategy

Partner with portfolio leadership to shape growth strategy across AI infrastructure opportunities. Responsibilities include:

  • Developing pursuit strategies and infrastructure win themes.
  • Leading technical volumes and solution architecture for proposals.
  • Joining orals as enterprise AI infrastructure authority.
  • Supporting customer engagements and modernization vision briefings.
  1. Support Delivery Excellence Once Pursuits Are Won

Provide handson technical leadership during delivery by:

  • Guiding engineering teams through environment setup.
  • Leading modernization plans and operating model transitions.
  • Overseeing performance, security, and reliability of inference environments.
  • Ensuring mission outcomes and operational readiness.

Here's What You Need:

  • Deep knowledge of hybrid and multicloud architectures, cloudnative platforms, and secure infrastructure modernization patterns.
  • Experience with highperformance computing, distributed training, and GPUaccelerated workloads.
  • Expertise in secure data onboarding, crossdomain operations, and AI pipeline orchestration.
  • Strong understanding of zerotrust architectures, data governance, and compliant secure environments.
  • Proven ability to operate at the intersection of mission needs, infrastructure engineering, and AI strategy.

Success Criteria

  • Modernized SUNet 2.0, HITSU, and JEDAIaligned infrastructure with measurable mission performance improvements.
  • Secure, scalable AI environments supporting rapid development, evaluation, and deployment of missioncritical models.
  • Growth in AI infrastructure pipeline and increased PWIN for major pursuits.
  • Successful delivery execution across awarded AI infrastructure programs.
  • Partner and customer recognition as the authoritative lead for Defense AI infrastructure.

What Accenture Federal Services employees say

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Benefits

Hours and flexibility

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