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Senior Systems Engineer

Chantilly, VA, US • Posted 7 hours ago • Updated 7 hours ago

Full Time

On-site

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Job Details

Skills

Distributed Computing
System Security
Cyber Security
Cloud Computing
Continuous Integration
Continuous Delivery
Machine Learning Operations (ML Ops)
Software Development
Systems Engineering
Large Language Models (LLMs)
Orchestration
Docker
Kubernetes
Computer Cluster Management
API
Data Governance
Amazon Web Services
Microsoft Azure
Google Cloud Platform
Google Cloud
IaaS
GPU
Computer Networking
Network Engineering
Software Security
Extract
Transform
Load
Artificial Intelligence
Machine Learning (ML)
Training
Communication
Collaboration
Mentorship

Distributed Computing
System Security
Cyber Security
Cloud Computing
Continuous Integration
Continuous Delivery
Machine Learning Operations (ML Ops)
Software Development
Systems Engineering
Large Language Models (LLMs)
Orchestration
Docker
Kubernetes
Computer Cluster Management
API
Data Governance
Amazon Web Services
Microsoft Azure
Google Cloud Platform
Google Cloud
IaaS
GPU
Computer Networking
Network Engineering
Software Security
Extract
Transform
Load
Artificial Intelligence
Machine Learning (ML)
Training
Communication
Collaboration
Mentorship

Summary

Overview

We are seeking an experienced Senior AI/ML Systems Engineer to design, integrate, deploy, and maintain enterprise-grade AI-enabled systems supporting cyber operations. This role combines systems engineering, software development, cybersecurity, cloud infrastructure, and AI/LLM integration to modernize existing architectures and deliver scalable, secure technical solutions.

What will you do?

Design, build, deploy, and maintain enterprise-grade software applications and systems.
Integrate modern AI and Large Language Model (LLM) capabilities into existing legacy architectures, APIs, and enterprise systems.
Design scalable system architectures capable of supporting AI/ML model inference, training, and distributed processing.
Develop and maintain containerized applications using Docker and Kubernetes.
Design and support Kubernetes clusters and other orchestration environments for highly available and scalable workloads.
Support technical solutions for offensive and/or defensive cyber operations.
Implement secure system architectures incorporating Zero Trust principles, secure API gateways, and data governance controls.
Design and support cloud infrastructure within AWS, Azure, and/or Google Cloud Platform (Google Cloud Platform).
Optimize cloud infrastructure for AI/ML workloads utilizing GPU instances, virtual CPUs, high-speed networking, and distributed computing resources.
Design and support data pipelines used for AI/ML model training and inference.
Apply network engineering principles, low-latency communication protocols, and system security practices to complex technical environments.
Collaborate with software engineers, cybersecurity professionals, cloud engineers, data scientists, and other technical teams.
Translate complex customer and mission requirements into scalable system architectures and technical designs.
Provide technical guidance and mentorship to junior engineers.
Support automation, software delivery, and deployment through CI/CD and MLOps/LLMOps practices.

Do you have what it takes?

Active TS/SCI with Polygraph
BS degree and 11-15 years of relevant experience
7+ years of experience in software development or systems engineering, including building, deploying, and maintaining enterprise-grade applications.
5+ years of experience supporting offensive and/or defensive cyber operations.
Demonstrated experience integrating AI and Large Language Models (LLMs) into existing applications, APIs, legacy architectures, or enterprise systems.
Hands-on experience with containerization and orchestration technologies, including:
- Docker
- Kubernetes
- Cluster management
Knowledge of Zero Trust architecture, secure API gateways, and data governance requirements associated with AI/LLM deployments.
Knowledge of cloud infrastructure platforms such as AWS, Azure, or Google Cloud Platform.
Understanding of cloud infrastructure supporting AI/ML and distributed workloads, including GPU instances, vCPUs, and high-speed networking.
Knowledge of network engineering fundamentals and low-latency protocols.
Understanding of system and application security principles.
Knowledge of data pipeline architectures supporting AI/ML model inference and training.
Demonstrated ability to translate complex customer requirements into scalable technical solutions and system designs.
Strong collaboration and communication skills with the ability to work across multidisciplinary technical teams.
Experience providing technical guidance or mentorship to junior engineers.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: RTL806649
Position Id: ea901e6f124ad0bb29d6d3d2734b37e7
Posted 7 hours ago

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