Technical Architect - Forward Deployed Engineering (AI)

Location: Based in Pleasanton, California, or Austin, Texas; work Monday through Thursday at client sites; travel up to 40%

California base salary: $170,000–$220,000 per year, plus performance bonus and equity.

Company

hirify.global provides an industrial AI autonomy platform based on a Computational Knowledge Graph, supporting energy, mining, manufacturing, aerospace, defense, and logistics customers with predictive intelligence and agentic decision-making.

What you will do

• Own end-to-end architecture for enterprise AI deployments, mapping the Computational Knowledge Graph, Model Hub, Agent Foundry, and Visual AI to customer workflows and data environments.
• Design knowledge graphs, agentic and generative AI workflows, Visual AI deployments, and industrial data integrations across SCADA, ERP, CMMS, PLM, historians, and related systems.
• Define reference architectures, technical standards, security controls, API contracts, scalability thresholds, model versioning, and disaster recovery requirements.
• Establish cloud, edge, on-premise, observability, evaluation, monitoring, drift detection, and model-serving architectures for production AI systems.
• Lead technical discovery and architecture workshops, advise senior customer stakeholders, and translate technical decisions into operational outcomes.
• Mentor Forward Deployed AI Engineers, resolve architectural conflicts, mitigate delivery risks, and feed reusable patterns into hirify.global’s delivery playbooks.

Requirements

• Deep hands-on experience designing and deploying enterprise AI or data platform solutions in industrial, OT, or critical infrastructure environments, with direct ownership of production architecture.
• Strong command of generative and agentic AI architectures, including LLM integration, agent orchestration, RAG pipelines, tool use, and reliability and safety patterns.
• Practical experience with industrial data integration, including MQTT, OPC-UA/DA, REST/GraphQL APIs, Kafka, Spark, and historian connectors.
• Experience with Docker, Kubernetes, MLOps pipelines, real-time inference, model monitoring, and AI/ML model deployment and serving infrastructure.
• Familiarity with industrial control systems and SCADA architecture, including data latency, network topology, and application constraints.
• Strong architecture skills across AWS, GCP, or Oracle Cloud, including edge and on-premise OT deployment models.
• Solid Python foundation and ability to review production code and collaborate with engineering teams during integration and debugging.
• Experience with graph data models, knowledge representation, semantic data integration, computer vision, and multi-modal AI.
• 8+ years in software engineering, systems architecture, or technical delivery, including at least 3 years as a lead architect or principal engineer with direct delivery accountability.
• Track record owning architecture for large-scale production enterprise AI or industrial systems; relevant sectors include energy, manufacturing, mining, aerospace, and defense.
• Certification or hands-on experience in data science, machine learning, or AI systems engineering.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field.
• Must be authorized to work in the United States without current or future company sponsorship.

Nice to have

• Master’s degree in a relevant technical field.
• Active U.S. Government security clearance.

Culture & Benefits

• Fast-scaling startup environment focused on AI innovation, collaboration, and professional growth.
• Comprehensive health, dental, and vision coverage.
• 401(k) participation, generous PTO, and company holidays.
• Ongoing learning and development opportunities.
• Performance bonus tied to account growth and delivery outcomes, plus stock-option equity.