Software Engineer V, AI Digital & Engineering Software

About this role

X-energy LLC conducts a thorough recruiting process and will never issue offers without interview to discuss qualifications and responsibilities. All applications will be submitted via our company career page, www.x-energy.com/careers/ . We will never ask you to provide payment information as part of the recruiting process. If anyone claiming to represent X-energy directs you in a manner otherwise, please contact us at www.x-energy.com/contact-us .

Job Description The AI & Digital Engineering Engineer contributes to X-energy's Artificial Intelligence (AI) Solutions Tiger Team, which accelerates Xe-100 nuclear reactor development workflows by designing and building production-ready, AI-native applications, agentic workflows, and the platform infrastructure that supports them. Working in a fast-paced, collaborative team environment, this engineer applies modern large language models, agentic AI, and cloud-native engineering to move X-energy from siloed documents toward a living, queryable engineering model spanning requirements, design, manufacturing, regulatory, and deployment processes. This role is essential to Xenergy's mission of becoming an AI-first organization and setting the industry standard for nuclear deployment speed and operational excellence.   Job Profile Tasks/Responsibilities: • Work collaboratively in a tiger-team environment to rapidly develop production-ready AI solutions. • Leverage Claude Code and AI-assisted development tools to accelerate development, prompt iteration, and maintenance tasks. • Apply knowledge of LLMs and AI systems to support the platform's AI-native architecture. • Partner with X-energy's systems-engineering, licensing, and quality-assurance organizations to ensure delivered solutions and AI-generated artifacts meet nuclear-engineering and regulatory expectations. • Document solutions, architecture decisions, and integration patterns, and support knowledge transfer to relevant technical teams. • Implement and maintain development best practices and quality standards. • Execute core tasks and responsibilities with minimal supervision in a fast-paced, team-oriented environment. • Perform work in accordance with X-energy quality assurance procedures. • Maintain professional demeanor and behavior at all times in all forms of communication. • Perform other duties as assigned by manager.

Specialization Tracks

Track A — Front-End & Full-Stack • Design and implement user interfaces using React, TypeScript, Tailwind CSS, and Vite. • Develop full-stack solutions connecting front-end interfaces to AWS backend services (Lambda, ECS, DynamoDB, and related services). • Create responsive dashboards and visualization tools for engineering workflows and AI agent interactions. • Implement real-time communication through WebSockets and REST APIs. • Architect maintainable, scalable front-end solutions for technical and engineering applications. • Implement secure authentication and authorization systems for enterprise applications. • Create and maintain CI/CD pipelines for web applications. • Build proof-of-concept demonstrations to validate solution approaches with stakeholders before full development. • Conduct user acceptance testing to ensure solutions meet real-world engineering requirements, and implement feedback loops between users and developers to iterate rapidly.

Track B — Agentic AI • Design and implement autonomous agent architectures using AWS Bedrock and related services. • Develop multi-turn agentic workflows — with reasoning, planning, memory management, and tool calling — optimized for engineering and business contexts. • Implement RAG and Graph-RAG systems (using Amazon Neptune or equivalent knowledge-graph infrastructure) for enhanced knowledge retrieval, requirements traceability, change-impact analysis, and design reuse. • Build AI applications for automated requirements extraction and classification, traceability-gap detection, verification-artifact generation, design-review assistance, and cross-discipline consistency checking. • Develop frameworks with LangChain, LangGraph, and AWS Strands for complex workflows, with safety and alignment controls appropriate for regulated engineering. • Curate golden datasets and benchmarks evaluating AI outputs against engineering ground truth (e.g., requirements quality per the INCOSE Guide, traceability completeness, configuration consistency). • Implement evaluation and safety pipelines using DeepEval, Ragas, or equivalent self-hostable frameworks that run within the GovCloud boundary and produce evidence suitable for NQA-1 and 10 CFR 50 Appendix B audit. • Develop MCP (Model Context Protocol) tools that expose engineering systems, reasoning capabilities, and agent workflows to the platform and external AI clients. • Research and implement cutting-edge techniques in autonomous agent development.

Track C — Data & Systems Integration • Design, build, and own the AWS-native data lakehouse / Common Data Environment using S3, Apache Iceberg (or equivalent open table format), AWS Glue Data Catalog, Lake Formation, and Athena as the governed substrate that AI agents and platform applications consume. • Develop the canonical engineering data model spanning Xe-100 requirements, bill-of-materials (BOM), configuration items, test and simulation results, quality records, and digital-thread artifacts, aligned with ISO 19650 information-container principles. • Architect schema governance — data contracts, schema registry, versioning, backward-compatibility rules, and deprecation workflows — across the platform's modular app ecosystem, and lead migration of platform services from per-app document stores to canonical lakehouse tables. • Implement a tiered data-classification model (public, internal, confidential, controlled, and restricted — including 10 CFR 2.390 and ITAR/EAR controlled tiers) enforced at the data layer via Lake Formation tag-based access control and row/column-level security. • Implement data line

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