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Ml Infrastructure Jobs in Virginia (NOW HIRING)

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 ...

Halvik-

AI/ML Subject Matter Expert

Vienna, VA · On-site

$100 - $130/hr

Make informed ML infrastructure decisions based on modeling techniques and issues * Write and test application code, develop ML models, and automate tests and deployment * Retrain, maintain, and ...

Halvik

AI/ML Subject Matter Expert

Vienna, VA · On-site

$195K - $210K/yr

Make informed ML infrastructure decisions based on modeling techniques and issues * Write and test application code, develop ML models, and automate tests and deployment * Retrain, maintain, and ...

Noblis

Data Scientist (AI/ML)

Chantilly, VA · On-site

Support production AI/ML infrastructure and operations , including platform maintenance, issue triage, troubleshooting, and user support. * Support Kubernetes-based platform operations , including ...

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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 Virginia?

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

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

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

What cities in Virginia are hiring for Ml Infrastructure jobs?

Cities in Virginia with the most Ml Infrastructure job openings:

Ml Infrastructure jobs near you
Infographic showing various Ml Infrastructure job openings in Virginia as of August 2026, with employment types broken down into 89% Full Time, 8% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

AI/ML Engineer, Mid

Reston, VA • On-site

Noblis
See more jobs at Noblisexternal

Full-time

Medical, Life, Retirement, PTO

Re-posted 19 days ago

Job description

Responsibilities
Noblis is seeking an experienced AI/ML Engineer to support mission-critical national security initiatives.
In this role, you will design, develop, and deploy advanced machine learning solutions while building the infrastructure required to operationalize AI capabilities in secure, production environments.
Job Responsibilities:
  • Model Development & Deployment
    • Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI.
    • Deploy, manage, and scale production ML workloads on Kubernetes.
    • Integrate AI/ML capabilities into full-stack applications using Python-based backend services and JavaScript frontend technologies.
    • Ensure model reliability, performance, and maintainability throughout the deployment lifecycle.
  • Infrastructure & Operations
    • Architect and implement cloud-native ML infrastructure on AWS.
    • Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring.
    • Deploy and support AI/ML systems within secure, classified, and high side environments.
  • Technical Leadership
    • Evaluate and adopt state-of-the-art AI/ML models, frameworks, and emerging technologies.
    • Architect scalable and resilient infrastructure to support evolving AI/ML workloads and mission requirements.
    • Establish and promote best practices for production-grade machine learning (ML) systems, including security, observability, and governance.
    • Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams.

Required Qualifications
  • Active Top Secret/SCI (TS/SCI) clearance with a current Polygraph.
  • Bachelor's degree with 5 years of related experience; OR Master's degree with 3 years of related experience; OR associate's degree with 8 years of related experience; OR High School diploma/GED with 11 years of related experience.
  • Experience deploying machine learning (ML) models to production, including large language models (LLMs)
  • Strong proficiency with machine learning (ML) frameworks and containerization technologies (e.g., PyTorch, Docker, and Kubernetes)
  • Full-stack software development experience using Python and JavaScript
  • Working knowledge of AWS cloud services and infrastructure
  • Demonstrated experience implementing MLOps and DevOps best practices, including CI/CD, model deployment, monitoring, and automation
  • U.S. Citizenship is required

Desired Qualifications
  • Expert-level proficiency in Python with extensive experience across leading machine learning (ML) frameworks, including TensorFlow, PyTorch, and scikit-learn
  • Proven ability to design and implement end-to-end machine learning (ML) pipelines, spanning data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
  • Extensive experience with large language models (LLMs), including fine-tuning, prompt engineering, retrieval-augmented generation (RAG), agentic workflows, and responsible AI practices
  • Expertise in advanced machine learning (ML) techniques, including deep learning, reinforcement learning, generative models, ensemble methods, and modern model optimization approaches
  • Proven track record of designing and implementing production-grade MLOps infrastructure, including automated model retraining, monitoring, drift detection, and CI/CD pipelines using tools such as MLflow, Kubeflow, and SageMaker Pipelines
  • Hands-on experience architecting and deploying scalable machine learning (ML) solutions on cloud platforms (e.g., AWS SageMaker, Azure Machine Learning, Google Vertex AI) with a focus on scalability, reliability, and cost optimization
  • Demonstrated experience leading technical architecture decisions and mentoring engineers on machine learning (ML) best practices, software engineering standards, experimentation, code quality, and research methodology
  • Strong background in distributed computing and big data technologies such as Apache Spark, Ray, and Dask for efficient model training and inference
  • Proficiency with containerization and orchestration technologies, including Docker and Kubernetes, to support scalable model serving, A/B testing, and canary releases/deployments.
  • Demonstrated ability to translate complex business problems into well-scoped ML solutions, communicating trade-offs, risks, and ROI to executive stakeholders
  • Experience contributing to or publishing applied ML research, patents, conference presentations, or open-source projects
  • 7+ years of experience designing, developing, and deploying machine learning systems at scale in production environments

Overview
Overview
Noblis and our wholly owned subsidiaries, Noblis ESI and Noblis MSD, take on some of the nation's toughest challenges, delivering advanced solutions to our customers' most critical missions. We bring together leading scientific, engineering, and management expertise in a culture grounded in objectivity and collaboration, ensuring our work creates lasting impact across federal missions.
We work with a broad range of government agencies in the defense, intelligence, and federal civilian sectors. Learn more and find opportunities at careers.noblis.org
Why Work at Noblis
At Noblis, we share a passion for excellence and innovation, and we create an environment where people can do meaningful work while maintaining the balance that keeps them energized and fulfilled. We seek out individuals with a natural curiosity and desire to collaborate and learn. We believe our people are our greatest strength, and we consistently seek exceptionally skilled, mission-driven professionals who care deeply about doing work that enriches lives and makes our nation safer.
Noblis has earned numerous workplace awards for our culture, our commitment to employee well-being, and our dedication to meaningful, impactful work. We also maintain a drug-free workplace.
Remote/hybrid status is subject to change based on Noblis and/or government requirements.
Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment without regard to race, color, ethnicity, sex, age, national origin, religion, physical or mental disability, pregnancy/childbirth and related medical conditions, veteran or military status, or any other characteristics protected by applicable federal, state, or local law.
If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact us.
EEO is the Law | E-Verify | Right to Work
Total Rewards
At Noblis we recognize and reward your contributions, provide you with growth opportunities, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, and work-life programs. Our award programs acknowledge employees for exceptional performance and superior demonstration of our service standards. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in our benefit programs. Other offerings may be provided for employees not within this category. We encourage you to learn more about our total benefits by visiting the Benefits page on our Careers site.
Compensation at Noblis is determined by various factors, including but not limited to, the combination of education, certifications, knowledge, skills, competencies, and experience, internal and external equity, location, clearance level, as well as contract-specific affordability, organizational requirements and applicable employment laws. The projected compensation range for this position is based on full time status. For part time or on-call staff, compensation is proportionately adjusted based on hours worked. While monetary compensation is important, it's just one component of Noblis' total compensation package.
Posted Salary Range
USD $132,900.00 - USD $207,750.00 /Yr.

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