{"id":1258691,"url":"https://alion.io/job/x-energy-software-engineer-v-ai-digital-engineering-software","title":"Software Engineer V, AI Digital & Engineering Software","company":{"id":1789545,"name":"X-energy","domain":"x-energy.com","url":"https://alion.io/company/x-energy","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Rockville, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":220000,"max":245000,"currency":"USD","period":"year","gross":null,"usd_annual":245000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon DocumentDB","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Neptune","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache HTTP Server","optional":false},{"name":"Apache Hudi","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"API Gateway","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Confluence","optional":false},{"name":"Datadog","optional":false},{"name":"DeepEval","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"GitLab","optional":false},{"name":"GitLab CI","optional":false},{"name":"Hallucination","optional":false},{"name":"IAM","optional":false},{"name":"JavaScript","optional":false},{"name":"Jira","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Linux","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Node JS","optional":false},{"name":"OpenSearch","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"React.js","optional":false},{"name":"Redis","optional":false},{"name":"Rest API","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Tailwind CSS","optional":false},{"name":"Terraform","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"Vite","optional":false},{"name":"WebSockets","optional":false},{"name":"Databricks","optional":true},{"name":"FedRAMP","optional":true},{"name":"Neo4j","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-08-18T00:00:00Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-10-02T04:02:49Z","board_verified":true,"closed_at":null,"days_open":46,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":46},"description":"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.\nJob Description\nThe 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, agenticworkflows, 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.\nJob Profile Tasks/Responsibilities:\nWork collaboratively in a tiger-team environment to rapidly develop production-ready AI solutions.\nLeverage Claude Code and AI-assisted development tools to accelerate development, prompt iteration, and maintenance tasks.\nApply knowledge of LLMs and AI systems to support the platform's AI-native architecture.\nPartner 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.\nDocument solutions, architecture decisions, and integration patterns, and support knowledge transfer to relevant technical teams.\nImplement and maintain development best practices and quality standards.\nExecute core tasks and responsibilities with minimal supervision in a fast-paced, team-oriented environment.\nPerform work in accordance with X-energy quality assurance procedures.\nMaintain professional demeanor and behavior at all times in all forms of communication.\nPerform other duties as assigned by manager.\nSpecialization Tracks\nTrack A - Front-End & Full-Stack\nDesign and implement user interfaces using React, TypeScript, Tailwind CSS, and Vite.\nDevelop full-stack solutions connecting front-end interfaces to AWS backend services (Lambda, ECS, DynamoDB, and related services).\nCreate responsive dashboards and visualization tools for engineering workflows and AI agent interactions.\nImplement real-time communication through WebSockets and REST APIs.\nArchitect maintainable, scalable front-end solutions for technical and engineering applications.\nImplement secure authentication and authorization systems for enterprise applications.\nCreate and maintain CI/CD pipelines for web applications.\nBuild proof-of-concept demonstrations to validate solution approaches with stakeholders before full development.\nConduct user acceptance testing to ensure solutions meet real-world engineering requirements, and implement feedback loops between users and developers to iterate rapidly.\nTrack B - Agentic AI\nDesign and implement autonomous agent architectures using AWS Bedrock and related services.\nDevelop multi-turn agentic workflows - with reasoning, planning, memory management, and tool calling - optimized for engineering and business contexts.\nImplement 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.\nBuild AI applications for automated requirements extraction and classification, traceability-gap detection, verification-artifact generation, design-review assistance, and cross-discipline consistency checking.\nDevelop frameworks with LangChain, LangGraph, and AWS Strands for complex workflows, with safety and alignment controls appropriate for regulated engineering.\nCurate golden datasets and benchmarks evaluating AI outputs against engineering ground truth (e.g., requirements quality per the INCOSE Guide, traceability completeness, configuration consistency).\nImplement 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.\nDevelop MCP (Model Context Protocol) tools that expose engineering systems, reasoning capabilities, and agent workflows to the platform and external AI clients.\nResearch and implement cutting-edge techniques in autonomous agent development.\nTrack C - Data & Systems Integration\nDesign, 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.\nDevelop 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.\nArchitect 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.\nImplement 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.\nImplement data lineage and provenance tracking (OpenLineage, AWS DataZone, or equivalent), supporting NQA-1 audit and 10 CFR 50 Appendix B traceability.\nBuild Change Data Capture (CDC) and event-driven pipelines that synchronize authoritative systems into the lakehouse using EventBridge, AWS DMS, Kinesis, and Glue streaming.\nDesign 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.\nParse, 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).\nDesign 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.\nDefine API contracts, versioning strategies, and B2B integration agreements; monitor and troubleshoot cross-organizational data flows, including partner-side connectivity, schema drift, and SLA compliance.\nEstablish data-quality standards, validation pipelines, and metric dashboards that feed AI agent eval harnesses and NRC submission workflows.\nTrack D - Platform Infrastructure & DevOps\nDesign, build, and maintain containerized applications using Docker, including image building, testing, versioning, and optimization for production deployment.\nDevelop and maintain GitLab CI/CD pipelines, including runner configuration, pipeline optimization, monitoring dashboards, and automated testing workflows.\nArchitect and manage AWS infrastructure using Terraform, including ECS, ECR, VPC, EC2, ALB/NLB, and other cloud services.\nAdminister and optimize AWS data services including DocumentDB, OpenSearch, Redis, Aurora Postgres, DynamoDB, and S3.\nImplement and maintain comprehensive monitoring, alerting, and observability solutions using Datadog and CloudWatch.\nManage security and compliance requirements, including ACM (AWS Certificate Manager) certificate management and security best practices across all infrastructure.\nLead release engineering efforts - versioning strategies, deployment automation, and rollback procedures - and establish cloud development lifecycle practices across platforms.\nCreate testing frameworks for validating system behavior in serverless environments and configure/optimize container orchestration services for application deployment.\nImplement versioning and governance for code and dependencies using version control systems.\nCollaborate with development teams to optimize application performance, troubleshoot production issues, and implement infrastructure improvements.\nParticipate in on-call rotation to ensure system reliability and rapid incident response.\nJob Profile Minimum Qualifications:\nRequired of all applicants\nBachelor's degree in Computer Science, Artificial Intelligence, Systems Engineering, Engineering, or a related field from an accredited university or college.\nTypically, fifteen years of relevant software engineering experience, including experience in one or more of the specialization tracks described above.\nExperience with Git, GitLab, CI/CD, and modern development workflows.\nProficiency with Jira, Confluence, and AI-enhanced development environments (Claude Code or similar AI-assisted development tools).\nExperience working in collaborative, fast-paced development teams.\nStrong written and verbal communication skills, including the ability to explain complex technical concepts to both technical and non-technical audiences.\nAbility to work a hybrid schedule in the Rockville, MD office Tuesday, Wednesday, and Thursday.\nAdditional qualifications by track (meet the requirements of at least one track)\nTrack A - Front-End & Full-Stack\nStrong expertise in React, TypeScript, and modern JavaScript development; experience with Vite, Bun, and Tailwind CSS.\nProficiency implementing WebSockets and REST APIs.\nBackground developing user interfaces for technical or engineering applications.\nExperience with AWS services for web-application deployment.\nTrack B - Agentic AI\nDeep expertise in modern large language models, agentic architectures, and multi-turn agent workflows.\nHands-on experience with at least one of AWS Bedrock, AWS Strands, LangChain, LangGraph, or LlamaIndex.\nProficiency with RAG and Graph-RAG, including vector search, hybrid retrieval, re-ranking, and chunking.\nProficiency in TypeScript and Python; familiarity with Node.js/Express and a Python web framework (FastAPI or similar).\nAI 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.\nStrong understanding of AWS AI services, particularly AWS Bedrock.\nTrack C - Data & Systems Integration\nStrong 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.\nExpert-level experience with at least one open table format (Apache Iceberg, Delta Lake, or Apache Hudi), including schema and partition evolution.\nExperience designing canonical data models in a regulated or engineering-heavy domain, with schema governance, data contracts, and multi-producer/multi-consumer patterns.\nExperience building production ETL pipelines and connectors with schema evolution, error recovery, and observability.\nHands-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.\nIdentity federation and B2B authentication (SAML, OIDC, SCIM, cross-account IAM); familiarity with enginee...","description_format":"text","description_chars":16735,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["401k plan","Equity","Vision insurance"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Energy & Utilities","Nuclear Energy"],"lifecycle":[{"event":"open","at":"2026-09-25T19:45:00Z"}],"liveness":{"score":25,"band":"fade","label":"Fading","p_open":1,"p_active":0.697,"p_room":0.36,"age_days":45,"expected_fill_days":29,"reasons":["conf:1","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-02T05:45:00Z"},"pay":{"stated_usd_annual":245000,"is_top_pay":true},"html_url":"https://alion.io/job/x-energy-software-engineer-v-ai-digital-engineering-software","json_url":"https://alion.io/job/x-energy-software-engineer-v-ai-digital-engineering-software.json","meta":{"generated_at":"2026-10-03T04:10:30Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4301,"day_limit":5000,"remaining_today":699,"minute_limit":60,"resets_at":"2026-10-04T00:00:00Z"}}}