Location: Chantilly, VA
Security Clearance: Active TS or higher [Required]
Job Type: Full-Time
Target Salary Range*:$145,000 - $210,000
*This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary
Position Overview
The AI Solutions Architect (Lead) designs and governs end-to-end AI architectures, solution patterns, and reference designs. This role leads the integration of AI capabilities across cloud platforms, enterprise applications, and complex operational environments, guiding engineering and data teams from prototype through production. The architect ensures security, scalability, governance, and zero trust alignment while communicating architectural decisions to executives, mission owners, and technical teams.
Key Responsibilities
AI Architecture and Solution Design
- Design and govern end-to-end AI architectures, solution patterns, and reference designs.
- Architect multistep, agentic AI workflows involving retrieval, tool calling, and automated reasoning loops.
- Evaluate emerging AI technologies and recommend appropriate technical stacks.
Enterprise Integration and Production Delivery
- Lead integration across Azure, AWS, GCP, APIs, enterprise applications, and orchestration frameworks.
- Guide engineering and data teams from prototype through production deployment.
Security and Governance
- Ensure security, scalability, governance, and zero trust alignment across AI systems.
Technical Leadership and Stakeholder Communication
- Communicate architectural decisions to executives, mission owners, and technical teams.
Qualifications
Education
- Bachelor’s degree required.
Experience
- Minimum of 10 years of experience.
- Experience in AI/ML engineering, cloud architecture, or data engineering.
- Proven experience designing production-grade AI systems and microservice architectures.
- Experience applying responsible AI, governance, and enterprise security frameworks.
- Development experience with Python, .NET/C#, and related languages.
Skills
- Expertise in AI/ML frameworks, LLMs, vector databases, retrieval systems, and distributed systems.
- Proficiency in Python, backend engineering, and API design.
- Familiarity with MLOps, DevOps, and platform engineering practices.
- Understanding of agentic AI, tool calling, and multi-agent orchestration.
Enterprise Infrastructure and Cloud Platforms
- Technical capabilities supporting air-gapped, hybrid cloud, and on-premises operational environments, including complex multi-enclave ecosystems.
- Knowledge of AWS GovCloud, SC2S, C2S, and GCP cloud platforms.
- Nutanix host and cluster configuration, management through Prism Central, and NDFS configuration and management.
- Technical capabilities supporting locally hosted GPU servers and multitenant cross-domain solutions (CDS).
- Infrastructure as code using Terraform and Ansible.
- Containerization and orchestration using Docker, Kubernetes, and OpenShift.
- Cloud cost optimization techniques.
- Knowledge of Linux distributions, including Rocky Linux, Red Hat, Amazon Linux, and Ubuntu; Windows; and macOS.
Networking, Identity, and Security
- Cisco switching, routing, firewalling, and site-to-site connectivity, including IPsec, DMVPN, and SD-WAN.
- Cloud networking and VPC configuration.
- Cloud-native security implementation and security accreditation processes.
- Identity and access services, including Microsoft Active Directory, LDAP/OpenLDAP, Entra ID, and Keycloak.
- High-assurance cross-domain and encryption solutions, including TACLANE and NCAP.
Application Platforms and Operations
- Advanced GitLab CI/CD pipelines and DevSecOps practices.
- Enterprise database services, including RDS, RDS Aurora, and DynamoDB.
- Serverless architectures using Lambda.
- Monitoring, logging, and data platforms, including Elasticsearch, OpenSearch, and Splunk.
- Microservices architecture and API Gateway implementation.
Preferred Qualifications
- Experience in federal, defense, or highly regulated environments.
- Knowledge of GPU acceleration, scalable model serving, or distributed training.
- Experience with enterprise-scale design systems, event-driven architectures, or service meshes.
- Cloud certifications in AWS, Azure, or GCP.