AI/ML Engineer – Public Sector
unstructured.io
full-time
Posted on:
Location: Florida, North Carolina • 🇺🇸 United States
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
AnsibleAWSAzureCloudGoogle Cloud PlatformKubernetesPythonTerraformTypeScript
About the role
- Support US government clients (Department of Defense and national security community) delivering complex software implementations on government networks
- Test, evaluate, and develop various models and implementation architectures for use on US government networks
- Develop evaluation and assessment tools and frameworks to measure models across tasks and knowledge sets
- Identify, propose, and implement modifications of existing models and implementation frameworks to optimize for new tasks
- Lead conceptualization of traditional and agentic implementation strategies for cloud and on-premises model deployments within broader system architectures
- Lead and optimize distributed ML workloads on multiple government cloud and non-cloud infrastructures
- Align AI/ML deployments with FedRAMP, NIST 800-53, FISMA, and DISA SRG, maintaining strict security standards
- Create reference architectures and deployment patterns to streamline ML adoption across government agencies
- Translate mission objectives into ML-focused technical specifications and project plans
- Apply advanced security controls and zero-trust architectures to protect ML pipelines and data
- Continuously assess ML workloads for performance, cost, and security improvements, driving ongoing refinement
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field (Master’s or PhD a plus)
- 4+ years of experience in AI/ML engineering, MLOPS, systems architecture, or similar technical roles
- 2+ years of experience working with government networks and security requirements
- TS Active Clearance required
- Ability to travel
- Understanding of government security frameworks (FedRAMP, NIST 800-53, FISMA, DISA SRG) and how they apply to ML workloads
- History of leading or delivering high-impact ML initiatives in enterprise or government environments; preference for experience assessing performance of alternative models, architectures, and implementation strategies
- Familiar with AWS, Azure, and/or GCP services for ML workloads
- Experience with government cloud offerings (AWS GovCloud, Azure Government, etc.)
- Multi-cloud ML architecture design and implementation
- Cloud cost optimization and resource governance for AI/ML
- Knowledge of Kubernetes administration (EKS, AKS, GKE)
- Container security and compliance for ML containers
- Experience with IaaC such as Terraform, Ansible, Pulumi for provisioning ML environments
- CI/CD pipeline integration for automated ML model deployment
- Network security for ML pipelines
- Security automation and continuous compliance monitoring
- Python proficiency (ML model development, data processing, pipeline orchestration)
- API design and development for ML services
- Debugging and performance optimization in ML systems
- Code review and quality assessment
- Executive-level communication skills and stakeholder management
- Strategic thinking, technical innovation, and leadership in ML/AI settings