Senior Staff Engineer System Architect
Shield AI is a U.S. defense-technology company developing mission-autonomy software and autonomous aircraft for military and allied operations.
Funding history
Investors
About Shield AI
Founded in 2015, Shield AI builds Hivemind autonomy software and V-BAT and X-BAT aircraft for operations in contested, GPS- and communications-denied environments. Its current site also presents Aechelon synthetic-reality simulation and Vision Systems detection and tracking products.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will own and evolve the architecture of an AI development ecosystem. You will define architecture, data models, interfaces, APIs, SDK concepts, standards, AI-assisted workflows, and scalable deployment patterns. You will preserve traceability, security, reproducibility, and architectural integrity across implementation and integration.
Requirements
- 10+ years of experience in software or software-intensive systems development, design, or architecture.
- Experience architecting complex software platforms, developer ecosystems, SDKs, APIs, AI/ML platforms, or distributed systems.
- Experience developing AI/ML solutions using synthetic and real-world data.
- Experience in data modeling and data architecture, including metadata, lineage, provenance, versioning, and lifecycle management.
- Understanding of end-to-end traceability, reproducibility, and auditability.
- Experience applying Generative AI, AI assistants, or agents to development workflows.
- Understanding of Kubernetes, workload schedulers, containerization, infrastructure as code, and object/data storage.
- Technical leadership and communication skills.
Responsibilities
- Own and evolve the architecture of the AI development ecosystem.
- Define and maintain architecture products in an MBSE environment.
- Establish architectural patterns, interfaces, APIs, SDK concepts, and technical standards.
- Define data and metadata architecture for the AI development lifecycle.
- Ensure traceability across datasets, configurations, model artifacts, evaluations, software versions, and deployments.
- Architect GenAI-enabled and agentic development workflows with human oversight and security.
- Define scalable AI/ML workload architecture across cloud, HPC, and on-premises infrastructure.
- Ensure deployment capability in classified, air-gapped, disconnected, and constrained environments.
- Guide architecture for data, model, evaluation, validation, and deployment workflows.
- Review detailed software and data designs and resolve cross-team architectural issues.
Benefits
- Equity