Senior Staff Engineer, System Architect
Own the architecture of an AI development ecosystem that supports data, model, simulation, and deployment workflows for autonomous capabilities. The role requires 10+ years of software architecture experience, strong AI/ML and data architecture expertise, and the ability to lead across complex engineering teams.
About the job
Responsibilities
- 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.
- Establish architectural patterns, interfaces, APIs, SDK concepts, and technical standards across software, AI/ML, data, simulation, and deployment capabilities.
- Define data and metadata architecture, including schemas, relationships, lineage, provenance, versioning, and lifecycle management.
- Ensure traceability across datasets, training configurations, model artifacts, evaluations, software versions, and deployed capabilities.
- Architect GenAI-enabled and agentic workflows with human oversight, security, verification, and traceability.
- Define scalable AI/ML workload architectures across cloud, high-performance computing, and on-premises infrastructure.
- Architect deployment and operation in classified, air-gapped, disconnected, and constrained environments.
- Guide workflows spanning data ingestion and curation, synthetic data generation, model training and tuning, evaluation, optimization, validation, and deployment.
- Review and approve software and data designs, resolve cross-team architectural issues, and maintain architectural integrity.
- Partner with software, AI/ML, data, systems, product, and technical leadership teams.
Requirements
- 10+ years of experience in software or software-intensive systems development, design, and/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 across data, models, software, and deployments.
- Experience applying Generative AI, AI assistants, or agents to software, AI/ML, or engineering workflows.
- Practical understanding of Kubernetes, Slurm or comparable workload schedulers, containerization, infrastructure as code, and S3-compatible object/data storage.
- Strong technical leadership and communication skills.
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.
- Experience with VLMs, VLAs, world models, foundation models, or related modern AI models.
- Experience with classified, air-gapped, disconnected, or restricted-network environments.
- Experience with hybrid-cloud, multi-cloud, on-premises, or edge deployment architectures.
- Experience with autonomy, robotics, aerospace, unmanned systems, or Physical AI applications.
- Experience with 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.
Skills
Software Architecture, AI/ML, Data Architecture, Kubernetes, Slurm, Containerization, Infrastructure As Code, Amazon S3, MLOps, Distributed Training, Model Registries, Sysml, Cameo/Magicdraw, Generative AI, APIs
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