AI Solutions Architect

About Aalo Atomics Aalo Atomics is pioneering a new era in clean energy with factory-fabricated microreactors designed to deliver affordable, scalable, and reliable nuclear power. Our mission is to make nuclear energy globally accessible, starting with the Aalo-1, a 10 MWe reactor leveraging cutting-edge safety, modularity, and efficiency. Based in Austin, TX, we’re rapidly growing as we work to deploy the world’s first fleet of advanced microreactors. Join us and help revolutionize energy for a sustainable future. About the role We’re hiring an AI Solutions Engineer to work directly with internal stakeholders and turn real workflows into production AI systems. At Aalo, AI is becoming part of how the company operates day to day. This role focuses on rapidly understanding workflows, building solutions with AI-native tools, and productizing systems for internal teams across the business. This role sits at the intersection of engineering, operations, and stakeholder collaboration. You will work as part of a small, highly collaborative AI team and partner directly with internal clients to turn meetings, workflows, data, and rough prototypes into working systems. Your job is to work closely with stakeholders and the broader AI team to move quickly from requirements to implementation, while making sure the resulting workflows are useful, maintainable, adopted, and trusted in day-to-day use. This is a forward-deployed software and systems engineering role with strong workflow, integration, and productization responsibilities. Examples of the systems and workflows this role may touch include: AI-assisted engineering workflows, document control, and approval automation Multi-agent licensing review, regulatory analysis, and contracting workflows Factory orchestration across manufacturing, inventory, procurement, quality, traceability, shipping, and receiving systems Supplier qualification, approved-vendor management, and procurement workflows Enterprise business systems across HR, finance, accounting, ERP, spend-management, and audit-sensitive workflows Engineering data systems including PLM, CAD, technical documents, and revision-controlled approval flows Meeting intelligence including transcription, summarization, and action-item capture Tuning advanced simulation and modeling frameworks to improve the statistical accuracy and speed of nuclear site characterization Cross-functional AI workflows that connect siloed systems and teams through automation Common technology categories and engineering patterns in the stack include: Backend application development and scripting Internal web applications and workflow interfaces Relational and document-oriented databases APIs, integration services, and service-oriented backend systems AI models, coding agents, agent harnesses, and orchestration frameworks Enterprise integrations, browser automation, and workflow orchestration Containers, CI/CD, and internal deployment patterns Shared platform services for observability, evaluation, identity, and secure model access Cloud platforms, secure storage, enterprise authentication, and secrets management Data pipelines, background jobs, and automation glue across internal systems What you'll do Sit down with internal stakeholders, learn their workflows, and translate ambiguous problems into working requirements and implementation plans Build and iterate AI-enabled workflows from first pass to production through integration, edge-case handling, testing, hardening, rollout, and adoption support Work in a rapid delivery loop where AI-generated implementations are refined quickly into reliable internal tools Review, debug, and refine AI-generated code and workflows to ensure they are useful, maintainable, and safe to operate Use shared platform capabilities for observability, evaluation, and secure model access while helping improve them through real-world feedback Work closely with platform engineers and internal stakeholders to connect systems across dep