Engineering Assistant Jobs in Frederick, MD (NOW HIRING)

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 Software Engineer III supports 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 and under the direct guidance and general supervision of senior engineers and technical leadership, this engineer applies modern large language models, agentic AI, and cloud-native engineering practices to help move X-energy from siloed documents toward a living, queryable engineering model spanning requirements, design, manufacturing, regulatory, and deployment processes. This role contributes to X-energy's mission of becoming an AI-first organization and to the broader effort to set the industry standard for nuclear deployment speed and operational excellence, operating under general supervision with moderate latitude for independent judgment on routine and clearly defined tasks.

Job Profile Tasks/Responsibilities:

• Work collaboratively in a tiger-team environment to 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 help 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 under general supervision, exercising moderate latitude for independent judgment on routine and clearly defined assignments 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 TracksTrack 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), working under the direct guidance of senior engineers on more complex integrations.
• Create responsive dashboards and visualization tools for engineering workflows and AI agent interactions.
• Implement real-time communication through WebSockets and REST APIs.
• Assist in the design of maintainable, scalable front-end solutions for technical and engineering applications.
• Implement secure authentication and authorization systems for enterprise applications, with guidance from senior engineers on more complex requirements.
• 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 support 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, under the direction of senior engineers.
• Develop multi-turn agentic workflows - with reasoning, planning, memory management, and tool calling - 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 workflows using LangChain, LangGraph, and AWS Strands for routine and moderately complex use cases, applying safety and alignment controls appropriate for regulated engineering.
• Assist in curating golden datasets and benchmarks evaluating AI outputs against engineering ground truth (e.g., requirements quality per the INCOSE Guide, traceability completeness, configuration consistency).
• Assist with 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.
• Stay current with emerging techniques in autonomous agent development, escalating opportunities for adoption to senior engineers.

Track C - Data & Systems Integration

• Assist in the design and build of 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.
• Contribute to 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.
• Assist with schema governance - data contracts, schema registry, versioning, backward-compatibility rules, and deprecation workflows - across the platform's modular app ecosystem, under the guidance of senior engineers on 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.
• Assist with 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).
• Assist with identity federation design for external partners (SAML 2.0, OIDC, SCIM, AWS IAM Identity Center, Amazon Cognito, cross-account IAM) and help apply export control (ITAR, EAR, 10 CFR 810) and data residency requirements at the integration boundary, under senior guidance.
• Support definition of API contracts and versioning strategies; help monitor cross-organizational data flows, including partner-side connectivity, schema drift, and SLA compliance.
• Support data-quality standards, validation pipelines, and metric dashboards that feed AI agent eval harnesses and NRC submission workflows.

Track D - Platform Infrastructure & DevOps

• 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.
• Assist with the architecture of AWS infrastructure using Terraform, including ECS, ECR, VPC, EC2, ALB/NLB, and other cloud services, under the guidance of senior engineers.
• Administer and help optimize AWS data services including DocumentDB, OpenSearch, Redis, Aurora Postgres, DynamoDB, and S3.
• Implement and maintain monitoring, alerting, and observability solutions using Datadog and CloudWatch.
• Support security and compliance requirements, including ACM (AWS Certificate Manager) certificate management and security best practices across all infrastructure.
• Assist with release engineering efforts - versioning strategies, deployment automation, and rollback procedures - and help apply 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 support system reliability and incident response.

Product-management and technical-leadership activities are woven across all tracks, not assigned to a separate track. Depending on scope and under the guidance of senior engineers, an engineer in this role may: contribute to technical specifications and roadmaps aligned with engineering needs; serve as a technical point of contact between the development team and internal customers on assigned work; help track product metrics and success criteria; support evaluation of appropriate AWS services; produce system architecture diagrams and API documentation; participate in technical reviews and code walkthroughs; and develop technical prototypes and proof-of-concepts.

Job Profile Minimum Qualifications:

Required of all applicants

• Bachelor's degree in Computer Science, Artificial Intelligence, Systems Engineering, Engineering, or a related field from an accredited university or college.
• Typically, five 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.

Additional qualifications by track (meet the requirements of at least one track)

Track A - Front-End & Full-Stack

• Basic to working knowledge of React, TypeScript, and modern JavaScript development; familiarity with Vite, Bun, and Tailwind CSS.
• Working knowledge of implementing WebSockets and REST APIs.
• Some background developing user interfaces for technical or engineering applications.
• Familiarity with AWS services for web-application deployment.

Track B - Agentic AI

• Basic to working knowledge of modern large language models, agentic architectures, and multi-turn agent workflows.
• Some hands-on exposure to at least one of AWS Bedrock, AWS Strands, LangChain, LangGraph, or LlamaIndex.
• Familiarity with RAG and Graph-RAG, including vector search, hybrid retrieval, re-ranking, and chunking.
• Working proficiency in TypeScript and Python; familiarity with Node.js/Express and a Python web framework (FastAPI or similar).
• Familiarity with AI evaluation methodology (golden datasets, LLM-as-judge, red-teaming, hallucination detection) and exposure to eval pipelines using DeepEval, Ragas, Arize Phoenix, or equivalent self-hostable frameworks.
• Basic understanding of AWS AI services, particularly AWS Bedrock.

Track C - Data & Systems Integration

• Some experience with data lakes/lakehouses and ETL processes; familiarity with AWS data services including S3, Glue, Lake Formation, Athena, Aurora, and DynamoDB.
• Familiarity with at least one open table format (Apache Iceberg, Delta Lake, or Apache Hudi).
• Exposure to designing canonical data models in a regulated or engineering-heavy domain, with familiarity with schema governance and data contracts.
• Experience building or supporting ETL pipelines and connectors.
• Exposure to at least one enterprise engineering system (PLM, requirements management, ERP, MES - e.g., Teamcenter, Windchill, Polarion, DOORS, Jama, P6) integration with a data platform.
• Familiarity with identity federation and B2B authentication concepts (SAML, OIDC, SCIM, cross-account IAM); familiarity with engineering data-interchange formats (STEP, ReqIF, SysML XMI, or similar) is a plus.
• Basic understanding of data security, classification, lineage, and governance best practices.

Track D - Platform Infrastructure & DevOps

• Working experience with Docker containerization, including building, testing, and deploying applications.
• Experience with GitLab CI/CD systems (runner setup, pipeline configuration, troubleshooting).
• Basic to working proficiency with AWS, particularly ECS, ECR, VPC, and Terraform infrastructure-as-code; familiarity with AWS data services (DocumentDB, OpenSearch, Redis, Aurora Postgres, or DynamoDB).
• Some exposure to release engineering and deployment automation.
• Familiarity with monitoring and observability tools (Datadog preferred) and cloud-based alerting solutions.