Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai . Follow Shield AI on LinkedIn , X , Instagram , and YouTube . Job Description: Shield AI is seeking an experienced System Architect to own the architecture of Hivemind Forge – an AI development ecosystem and the segment of Hivemind that enables developers to build, train, tune, evaluate, optimize, and deploy learning-based autonomy solutions. You will join the Systems Engineering, Integration, & Test (SEIT) team in the Hivemind Enterprise organization. This ecosystem combines developer tools, APIs, data infrastructure, simulation, AI/ML workflows, and GenAI-enabled automation to accelerate the delivery of autonomous capabilities using Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, world models, foundation models, and other applied AI technologies. As the responsible architect, you will define the comprehensive architecture for this product area, maintain architectural integrity across development teams, and ensure the ecosystem can operate across enterprise cloud, high-performance computing, and secure or disconnected deployment environments. You will work at the intersection of software architecture, AI/ML, data architecture, systems engineering, developer experience, and Generative AI-enabled engineering to create an ecosystem that dramatically reduces the time required to transform mission needs and data into deployable, intelligent autonomous capabilities. What you'll do: Own and evolve the architecture of the Hivemind Forge AI development ecosystem. Define and maintain architecture products in an integrated model-based systems engineering (MBSE) environment, connecting architectural intent to engineering execution. Establish architectural patterns, interfaces, APIs, SDK concepts, and technical standards across software, AI/ML, data, simulation, and deployment capabilities. Define the data and metadata architecture underpinning the AI development lifecycle, including schemas, relationships, lineage, provenance, versioning, and lifecycle management. Ensure traceable pedigree across datasets, training configurations, model artifacts, evaluations, software versions, and deployed capabilities to support reproducibility and auditability. Architect GenAI-enabled and agentic development workflows that accelerate data curation, autonomy development, experimentation, evaluation, troubleshooting, and deployment while preserving human oversight, security, verification, and traceability. Define architectural patterns for integrating AI coding assistants, agents, foundation models, and natural-language interfaces with Hivemind development tools, APIs, SDKs, data, simulation, and engineering workflows. Define architecture for scalable AI/ML workloads across cloud, high-performance compute (HPC), and on-premises infrastructure, including orchestration, workload scheduling, containerization, storage, and data movement. Ensure the ecosystem can be deployed and operated in classified, air-gapped, disconnected, and other constrained enterprise environments while preserving security, configuration control, and reproducibility. Ensure AI-assisted and agentic development workflows preserve appropriate provenance, traceability, human oversight, verification, security, and reproducibility, particularly when contributing to deployed autonomous capabilities. Guide architecture for workflows spanning data ingestion and curation, synthetic data generation, model training and tuning, evaluation, optimization, validation, and deployment. Review and approve detailed software and data designs, resolve cross-team architectural issues, and maintain architectural integrity through implementation and integration. Partner with software, AI/ML, data, systems, product, and technical leadership teams to reduce the time from mission need to validated, deployable autonomy. Required qualifications: 10+ years of experience in software or software-intensive systems development, design, and/or architecture. Demonstrated 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 how data, training configurations, model artifacts, evaluations, software, and deployments must be connected to provide end-to-end traceability, reproducibility, and auditability. Experience applying Generative AI, AI assistants, or agents to software, AI/ML, or engineering development workflows. Practical understanding of technologies such as Kubernetes, Slurm or comparable workload schedulers, containerization, infrastructure as code, and object/data storage platforms such as S3-compatible systems. Strong technical leadership and communication skills, with the ability to guide detailed design and maintain alignment across multiple engineering teams. Preferred qualifications: Experience designing AI-native or agentic workflows, including tool use, orchestration, retrieval, structured outputs, evaluation, and human-in-the-loop controls. Experience with MLOps, distributed training, simulation, synthetic data generation, experiment tracking, or model registries. Direct experience developing, training, tuning, evaluating, applying, or deploying VLMs, VLAs, world models, foundation models, or related modern AI models. Experience designing, deploying, or operating software and AI/ML platforms in classified, air-gapped, disconnected, or restricted-network environments. Experience with hybrid-cloud, multi-cloud, on-premises, or edge deployment architectures. Experience architecting autonomy, robotics, aerospace, unmanned systems, or other Physical AI applications. Experience applying MBSE methods and tools, including SysML and Cameo/MagicDraw. Experience delivering defense, aerospace, safety-relevant, or other high-assurance systems requiring rigorous configuration management, verification, and traceability. Impact; You will establish the architectural foundation that enables Hivemind users to transform mission needs, data, AI models, and software into deployable autonomous capabilities—faster, repeatedly, and with confidence in how each capability was created, evaluated, and validated. #LI-DM2 #LE Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. ### Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

Requirements

Must-haves

Own and evolve the architecture of the Hivemind Forge AI development ecosystem.
Define and maintain architecture products in an integrated model-based systems engineering (MBSE) environment, connecting architectural intent to engineering execution.
Establish architectural patterns, interfaces, APIs, SDK concepts, and technical standards across software, AI/ML, data, simulation, and deployment capabilities.
Define the data and metadata architecture underpinning the AI development lifecycle, including schemas, relationships, lineage, provenance, versioning, and lifecycle management.
Ensure traceable pedigree across datasets, training configurations, model artifacts, evaluations, software versions, and deployed capabilities to support reproducibility and auditability.
Define architectural patterns for integrating AI coding assistants, agents, foundation models, and natural-language interfaces with Hivemind development tools, APIs, SDKs, data, simulation, and engineering workflows.
Define architecture for scalable AI/ML workloads across cloud, high-performance compute (HPC), and on-premises infrastructure, including orchestration, workload scheduling, containerization, storage, and data movement.
Ensure the ecosystem can be deployed and operated in classified, air-gapped, disconnected, and other constrained enterprise environments while preserving security, configuration control, and reproducibility.
Guide architecture for workflows spanning data ingestion and curation, synthetic data generation, model training and tuning, evaluation, optimization, validation, and deployment.
Review and approve detailed software and data designs, resolve cross-team architectural issues, and maintain architectural integrity through implementation and integration.
Partner with software, AI/ML, data, systems, product, and technical leadership teams to reduce the time from mission need to validated, deployable autonomy.
10+ years of experience in software or software-intensive systems development, design, and/or architecture.
Demonstrated 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.

Nice-to-haves

Experience designing AI-native or agentic workflows, including tool use, orchestration, retrieval, structured outputs, evaluation, and human-in-the-loop controls.
Experience with MLOps, distributed training, simulation, synthetic data generation, experiment tracking, or model registries.
Direct experience developing, training, tuning, evaluating, applying, or deploying VLMs, VLAs, world models, foundation models, or related modern AI models.
Experience designing, deploying, or operating software and AI/ML platforms in classified, air-gapped, disconnected, or restricted-network environments.
Experience with hybrid-cloud, multi-cloud, on-premises, or edge deployment architectures.
Experience architecting autonomy, robotics, aerospace, unmanned systems, or other Physical AI applications.
Experience applying MBSE methods and tools, including SysML and Cameo/MagicDraw.
Experience delivering defense, aerospace, safety-relevant, or other high-assurance systems requiring rigorous configuration management, verification, and traceability.
Pay within range listed + Bonus + Benefits + Equity

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlkubernetes

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Vision