Software Engineer V, AI Digital & Engineering Software

X-energy•Rockville, MD

4d•$220,000 - $245,000•Hybrid

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Systems Engineering, Engineering, or a related field from an accredited university or college.
Typically, fifteen years of relevant software engineering experience, including experience in one or more of the specialization tracks described above.
Experience with Git, GitLab, CI/CD, and modern development workflows.
Proficiency with Jira, Confluence, and AI-enhanced development environments (Claude Code or similar AI-assisted development tools).
Experience working in collaborative, fast-paced development teams.
Strong written and verbal communication skills, including the ability to explain complex technical concepts to both technical and non-technical audiences.
Ability to work a hybrid schedule in the Rockville, MD office Tuesday, Wednesday, and Thursday.
Strong expertise in React, TypeScript, and modern JavaScript development; experience with Vite, Bun, and Tailwind CSS.
Proficiency implementing WebSockets and REST APIs.
Background developing user interfaces for technical or engineering applications.
Experience with AWS services for web-application deployment.
Deep expertise in modern large language models, agentic architectures, and multi-turn agent workflows.
Hands-on experience with at least one of AWS Bedrock, AWS Strands, LangChain, LangGraph, or LlamaIndex.
Proficiency with RAG and Graph-RAG, including vector search, hybrid retrieval, re-ranking, and chunking.
Proficiency in TypeScript and Python; familiarity with Node.js/Express and a Python web framework (FastAPI or similar).
AI evaluation methodology (golden datasets, LLM-as-judge, red-teaming, hallucination detection) and hands-on implementation of eval pipelines using DeepEval, Ragas, Arize Phoenix, or equivalent self-hostable frameworks.
Strong understanding of AWS AI services, particularly AWS Bedrock.
Strong experience designing and implementing data lakes/lakehouses and ETL processes; deep expertise in AWS data services including S3, Glue, Lake Formation, Athena, Aurora, and DynamoDB.
Expert-level experience with at least one open table format (Apache Iceberg, Delta Lake, or Apache Hudi), including schema and partition evolution.
Experience designing canonical data models in a regulated or engineering-heavy domain, with schema governance, data contracts, and multi-producer/multi-consumer patterns.
Experience building production ETL pipelines and connectors with schema evolution, error recovery, and observability.
Hands-on experience integrating at least one enterprise engineering system (PLM, requirements management, ERP, MES — e.g., Teamcenter, Windchill, Polarion, DOORS, Jama, P6) with a data platform.
Identity federation and B2B authentication (SAML, OIDC, SCIM, cross-account IAM); familiarity with engineering data-interchange formats (STEP, ReqIF, SysML XMI, or similar).
Understanding of data security, classification, lineage, and governance best practices.
Hands-on experience with Docker containerization, including building, testing, and deploying production applications.
Proven experience administering GitLab CI/CD systems (runner setup, pipeline configuration, troubleshooting).
Strong proficiency with AWS, particularly ECS, ECR, VPC, and Terraform infrastructure-as-code; experience managing AWS data services (DocumentDB, OpenSearch, Redis, Aurora Postgres, or DynamoDB).
Demonstrated experience with release engineering and deployment automation.
Knowledge of monitoring and observability tools (Datadog preferred) and cloud-based alerting solutions.
Proficiency with Linux and/or macOS command-line environments and with container orchestration services.

Nice To Haves

Experience in the nuclear energy industry, aerospace, defense, or other highly regulated technical fields, including familiarity with NRC regulatory workflows.
Prior experience building AI products or internal platforms for engineering, manufacturing, or scientific use cases.
Working literacy in digital engineering: requirements (INCOSE Guide, ReqIF), MBSE (SysML v1/v2), ISO 19650 information management, digital thread, traceability, and V&V; familiarity with Cameo Systems Modeler, Capella, or equivalent MBSE tools.
Experience integrating PLM and requirements systems (Teamcenter, Jama) with platforms; experience with graph databases (Amazon Neptune, Neo4j) for traceability or cross-system entity resolution.
Familiarity with NQA-1, 10 CFR 50 Appendix B, or equivalent software-quality frameworks; experience designing AI evaluation harnesses or data pipelines that produce evidence suitable for regulatory audit.
Experience with AWS GovCloud and FedRAMP-authorized services; familiarity with ITAR, EAR, or 10 CFR 810 export control in technical work.
Experience with MCP (Model Context Protocol) tool development.
Background in customer-facing technical roles (solutions/forward-deployed engineering, technical account management, developer relations) at organizations such as Palantir, Databricks, Anduril, Snowflake, or equivalent.
Familiarity with Idaho National Laboratory's Deep Lynx / Nexus or similar nuclear digital-engineering frameworks.
INCOSE ASEP or CSEP certification; prior open-source contributions to AI, MBSE, or digital-engineering tooling.

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.
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.
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.
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 lineage and provenance tracking (OpenLineage, AWS DataZone, or equivalent), supporting NQA-1 audit and 10 CFR 50 Appendix B traceability.
Build Change Data Capture (CDC) and event-driven pipelines that synchronize authoritative systems into the lakehouse using EventBridge, AWS DMS, Kinesis, and Glue streaming.
Design and implement integrations between customer, partner, and supplier engineering systems (Siemens Teamcenter, PTC Windchill, Dassault 3DEXPERIENCE, Aras Innovator, Siemens Polarion, IBM DOORS/DOORS Next, Jama Connect, Oracle Primavera P6, Procore, SAP ERP, Box) and X-energy's platform and lakehouse, using AWS Glue, DMS, Kinesis, EventBridge, API Gateway, and Lambda.
Parse, normalize, and map specialized engineering data formats including STEP (ISO 10303), JT, IFC, ReqIF, SysML v2 XMI, QIF, DWG/RVT, and proprietary PLM exports, aligned with ISO 19650 information-exchange requirements (OIR, PIR, EIR).
Design identity federation for external partners (SAML 2.0, OIDC, SCIM, AWS IAM Identity Center, Amazon Cognito, cross-account IAM) and enforce export control (ITAR, EAR, 10 CFR 810) and data residency at the integration boundary.
Define API contracts, versioning strategies, and B2B integration agreements; monitor and troubleshoot cross-organizational data flows, including partner-side connectivity, schema drift, and SLA compliance.
Establish data-quality standards, validation pipelines, and metric dashboards that feed AI agent eval harnesses and NRC submission workflows.
Design, build, and maintain containerized applications using Docker, including image building, testing, versioning, and optimization for production deployment.
Develop and maintain GitLab CI/CD pipelines, including runner configuration, pipeline optimization, monitoring dashboards, and automated testing workflows.
Architect and manage AWS infrastructure using Terraform, including ECS, ECR, VPC, EC2, ALB/NLB, and other cloud services.
Administer and optimize AWS data services including DocumentDB, OpenSearch, Redis, Aurora Postgres, DynamoDB, and S3.
Implement and maintain comprehensive monitoring, alerting, and observability solutions using Datadog and CloudWatch.
Manage security and compliance requirements, including ACM (AWS Certificate Manager) certificate management and security best practices across all infrastructure.
Lead release engineering efforts — versioning strategies, deployment automation, and rollback procedures — and establish cloud development lifecycle practices across platforms.
Create testing frameworks for validating system behavior in serverless environments and configure/optimize container orchestration services for application deployment.
Implement versioning and governance for code and dependencies using version control systems.
Collaborate with development teams to optimize application performance, troubleshoot production issues, and implement infrastructure improvements.
Participate in on-call rotation to ensure system reliability and rapid incident response.