Systems Engineer (AI/ML)

About

Bring your leading-edge technology expertise and thirst for knowledge to GDIT's premier cybersecurity contract! We are looking for an innovative, forward-thinking engineer with experience in frontier technologies like AI/ML, Automation, and Cloud/DevOps. You will be in a role with a good deal of latitude to propose improved methods, implement new processes, and research/integrate AI-centric solutions to accelerate AI-driven cybersecurity.

How a Systems Engineer (AI/ML) Will Make an Impact

• Design and implement architectures that integrate AI/ML platforms, model-serving infrastructure, vector databases, and MLOps tooling into enterprise environments.
• Design, deploy, and maintain AI/ML platforms and supporting infrastructure across cloud, hybrid, and on-prem environments.
• Integrate AI services with existing enterprise systems, including SIEM/SOAR platforms, APIs, ticketing systems, and operational data sources.
• Build and maintain secure, scalable pipelines for AI workloads, telemetry, logs, and other structured or unstructured data.
• Automate infrastructure deployment, configuration management, and operational workflows using tools such as Terraform and Ansible.
• Support Kubernetes- and container-based environments used for AI applications and model serving.
• Work closely with cybersecurity teams to implement hardening, access controls, monitoring, and other security requirements for AI systems.
• Participate in threat modeling, risk assessments, and ATO-related activities for AI infrastructure and integrations.
• Monitor and troubleshoot issues across systems, networks, applications, and cloud environments.
• Develop operational documentation including architecture diagrams, runbooks, SOPs, and integration documentation.
• Advise technical leadership and mission stakeholders on implementation approaches, tradeoffs, and operational risks related to AI technologies.
• Mentor junior engineers and contribute to improving engineering and operational practices across the team.

What You'll Need to Succeed

• Education: Bachelor’s degree in Computer Science, Information Systems, Engineering, Cybersecurity, or a related field; equivalent experience also considered.
• Experience: 8+ years
• Required technical skills:
  • Hands-on experience deploying or integrating AI/ML platforms, services, or tooling into operational systems.
  • Strong background in systems engineering, infrastructure engineering, solutions architecture, or related technical roles in secure environments including:
    • Linux administration (RHEL, CentOS, Ubuntu)
    • Networking fundamentals including TCP/IP, DNS, VPNs, firewalls, routing, proxies, and load balancing
    • Containerization and orchestration technologies such as Docker and Kubernetes
    • Cloud infrastructure, primarily AWS, including IAM, networking, and security services
    • Infrastructure and configuration management tools such as Terraform, CloudFormation, or Ansible
    • Scripting or development experience with Python, Bash, PowerShell, Go, or similar languages
  • Experience with AI/ML infrastructure such as:
    • Model serving platforms and APIs (MLflow, SageMaker, TorchServe, Vertex AI, Azure ML, custom REST/gRPC services)
    • Logging and telemetry pipelines
    • Relational, NoSQL, object storage, or vector database technologies
  • CI/CD, Git workflows, artifact management, and automated deployment pipelines
  • Understanding of core cybersecurity concepts, including:
    • Authentication and authorization (SSO, SAML, OIDC, OAuth, RBAC)
    • Encryption, certificates, and key management
    • Security monitoring and SIEM/SOAR integrations
• Additional Requirements:
  • Strong troubleshooting and problem-solving skills
  • Ability to translate operational and security requirements into technical solutions
  • Comfortable working in fast-paced, highly collaborative environments
  • Strong written and verbal communication skills
  • Security clearance level: TS/SCI with Polygraph

Preferred Qualifications

• Experience supporting AI-driven cybersecurity use cases such as threat detection, anomaly detection, UEBA, or automated incident response
• Experience integrating hosted or on-prem LLM platforms
• Familiarity with retrieval-augmented generation (RAG), prompt engineering, and associated data pipelines
• Experience with cybersecurity platforms such as Splunk, Elastic, QRadar, EDR/XDR, IDS/IPS, or SOAR technologies
• Experience operating in regulated, mission-critical, or high-security environments

Location

Herndon, VA - customer site

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 401(k) plan with company match
• Paid time off
• Paid parental leave
• Paid military leave
• Paid bereavement leave
• Paid jury duty leave
• Short-term disability
• Long-term disability
• Life insurance
• Accidental death and dismemberment insurance
• Personal accident insurance
• Critical illness insurance
• Business travel and accident insurance