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Ml Infrastructure Jobs in Washington (NOW HIRING)

Saic

AI/ML Engineer

Chantilly, VA · On-site

$180 - $240/hr

Support model-agnostic infrastructure leveraging partnerships with AI Titans and existing enterprise solutions. Responsibilities include but are not limited to: * Provide expert guidance on AI/ML ...

SAIC

AI/ML Engineer

Chantilly, VA · Hybrid

$200K - $240K/yr

Support model-agnostic infrastructure leveraging partnerships with AI Titans and existing enterprise solutions. Responsibilities include but are not limited to: * Provide expert guidance on AI/ML ...

SAIC

AI/ML Engineer

Chantilly, VA · On-site

$200K - $240K/yr

Support model-agnostic infrastructure leveraging partnerships with AI Titans and existing enterprise solutions. Responsibilities include but are not limited to: * Provide expert guidance on AI/ML ...

SAIC

AI/ML Engineer

Chantilly, VA · On-site

$200K - $240K/yr

Support model-agnostic infrastructure leveraging partnerships with AI Titans and existing enterprise solutions. Responsibilities include but are not limited to: * Provide expert guidance on AI/ML ...

Noblis

AI/ML Engineer, Mid

Reston, VA · On-site

Infrastructure & Operations * Architect and implement cloud-native ML infrastructure on AWS. * Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment ...

Noblis

AI/ML Engineer

Reston, VA · On-site

Infrastructure & Operations * Architect and implement cloud-native ML infrastructure on AWS. * Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment ...

Noblis

AI/ML Engineer, Senior

Chantilly, VA · On-site

$107K - $146K/yr

Infrastructure & Operations * Architect and implement cloud-native ML infrastructure on AWS. * Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment ...

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Ml Infrastructure information

What is ML infrastructure?

ML Infrastructure refers to the underlying systems, tools, and processes that enable the development, deployment, and scaling of machine learning models. This includes data storage and management, computing resources, model training and serving environments, monitoring, and automation tools. ML Infrastructure ensures that data scientists and engineers can efficiently build, test, and maintain machine learning applications in a reliable and reproducible manner. It is a crucial foundation for organizations looking to operationalize AI and machine learning solutions at scale.

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

Professionals in ML Infrastructure often encounter challenges related to scaling systems to handle large volumes of data, ensuring reliable deployment pipelines, and maintaining reproducibility across different environments. They must also collaborate closely with data scientists and engineers to streamline workflows and address issues like version control and model monitoring. Staying updated with rapidly evolving tools and best practices is essential, and balancing stability with innovation is a frequent aspect of the role.

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

To thrive as an ML Infrastructure Engineer, you need a strong background in software engineering, cloud computing, and machine learning concepts, often supported by a degree in computer science or a related field. Proficiency with containerization tools (like Docker and Kubernetes), cloud platforms (such as AWS, GCP, or Azure), and CI/CD systems is critical. Excellent problem-solving, collaboration, and communication skills help you efficiently work with data scientists and DevOps teams. These skills and qualities are vital for building scalable, reliable ML systems that support rapid experimentation and deployment in production environments.

What is the difference between Ml Infrastructure vs Data Engineer?

AspectML InfrastructureData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; knowledge of cloud platformsBachelor's in CS, Software Engineering, or related; experience with databases and ETL tools
Work EnvironmentFocus on deploying and maintaining ML systems, cloud environments, and infrastructure toolsDesigning, building, and managing data pipelines and storage solutions
Industry UsageUsed in AI/ML teams to support model deployment and scalabilityUsed across data-driven organizations for data management and analytics

ML Infrastructure specialists focus on deploying, scaling, and maintaining machine learning systems and infrastructure, while Data Engineers primarily build and manage data pipelines and storage solutions. Both roles require technical skills and often collaborate, but their core responsibilities differ in focus and tools used.

What are popular job titles related to Ml Infrastructure jobs in Washington?

For Ml Infrastructure jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Ml Infrastructure jobs in Washington look for?

The top searched job categories for Ml Infrastructure jobs in Washington are:

What cities in Washington are hiring for Ml Infrastructure jobs?

Cities in Washington with the most Ml Infrastructure job openings:

Ml Infrastructure jobs near you
Infographic showing various Ml Infrastructure job openings in Washington as of August 2026, with employment types broken down into 89% Full Time, 7% Part Time, and 4% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Senior DevSecOps Engineer (ML Infrastructure)

Oslitandi Tech LLC

Washington, DC • On-site

$122K - $166K/yr

Full-time

Re-posted 4 days ago

Job description

Job Summary:
Oslitandi Tech LLC is a company that works with clients to achieve their tactical and strategic goals through sustainable technology solutions. The Senior DevSecOps Engineer will be responsible for designing and implementing a secure MLOps platform, ensuring compliance with DoD standards while utilizing Infrastructure as Code and container orchestration tools.
Responsibilities:
• Conceptualize, Design, Build, and Maintain a secure, automated, end-to-end MLOps pipeline leveraging tools like Kubeflow and MLflow for continuous model training, testing, and deployment.
• Create, manage, and support Infrastructure as Code (IaC) solutions utilizing Terraform and Ansible to reliably provision and manage complex platform deployments across varying DoD classification levels (IL5/IL6) and Cloud environments (AWS GovCloud/Azure).
• Implement, administer, and harden Kubernetes clusters (including networking, storage, and access controls) to strictly meet DoD STIGs and compliance standards (e.g., NIST 800-53, RMF).
• Integrate continuous security scanning tools (SAST/DAST) and vulnerability management directly into the GitLab CI/CD workflow to ensure a DevSecOps posture and enable automated DoD Iron Bank compliance.
• Deploy and manage service mesh technologies, such as Istio or Linkerd, to enforce mTLS, traffic management, and policy enforcement across containerized microservices.
• Develop, manage, and support automation solutions for infrastructure and application orchestration using scripting languages such as Python or Go.
Qualifications:
Required:
• A minimum of 5+ years of experience in DevOps, Cloud/Platform Engineering, or Site Reliability Engineering (SRE).
• At least 3+ years of direct, hands-on experience administering and deploying applications on Kubernetes.
• Expert-level proficiency in defining, deploying, and managing infrastructure using Terraform.
• Proficiency in scripting and development skills (Python or Go) for automation tasks and tooling.
• Experience with configuring and managing CI/CD pipelines, specifically GitLab CI/CD and container registries.
• Working knowledge of security controls, compliance standards, and hardening practices (STIGs, RMF, NIST).
• The candidate shall have a Bachelor's degree in Computer Science, Engineering, or a related technical field.
• Must possess active DoD 8570 IAT Level II certification (Security+ or equivalent) REQUIRED.
• Must be eligible for a U.S. Government Secret / TS Clearance.
Preferred:
• CKA (Certified Kubernetes Administrator) or AWS Certified Solutions Architect certification preferred.
Company:
Our company works with clients to achieve their tactical and strategic goals by unifying sustainable technology solutions which reduce costs, decrease cycle times, and seamlessly manage processes throughout the enterprise. Founded in , the company is headquartered in Washington, USA, with a team of 2-10 employees. The company is currently Early Stage.

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