远程工作雷达

AI/ML工程师(有秘密许可)— 应用AI与自动化

AI/ML Engineer (Active Secret) — Applied AI & Automation

AI开发工程全球可投
公司rackner
薪资未公开
工作地点Remote
地域资格全球可投
时区要求无特别要求
用工类型未标注
发布时间2 天前
数据来源Greenhouse
前往企业招聘页投递 →
全球可投:该职位未限制候选人所在地区。仍需注意薪资可能按地区折算,以及实际签约方式(正式雇佣 / 独立合同)。

AI/ML 工程师 — 应用人工智能与智能自动化

远程
需要活跃的机密安全许可

构建进入生产环境的 AI

Rackner 正在寻找一名 AI/ML 工程师,帮助塑造人工智能在联邦数据现代化环境中的评估、构建、治理和部署方式。

此职位超越了孤立的原型或模型开发。您将帮助确定 AI 在何处能创造实际的操作价值,是否 AI 是合适的解决方案,以及有前景的用例如何从概念进入生产。

您将有机会构建 AI 应用程序、代理、智能工作流和自动化,同时影响新兴 AI 和 LLM 技术的评估、治理、监控和采用方式。您的工作可能涵盖用例评估、解决方案设计、原型开发、测试、部署、负责任的 AI 控制以及生产后监控。

与工程、数据、网络安全、治理和任务团队合作,您将获得完整的 AI 交付生命周期的可见性,并帮助塑造其背后的技术和决策。

对于希望超越狭窄模型开发角色的工程师,这个职位提供了以下方面的接触:

应用 AI 工程 → AI/LLM 评估 → 智能自动化 → 负责任的 AI → 企业采用

您将帮助将新兴 AI 能力转化为支持实际操作需求的实用工具——而不是为了创新而创新。

然后我直接进入:

您将负责

  • 将操作挑战转化为实际的 AI 用例、技术需求和解决方案设计
  • 构建并部署 AI 应用程序、代理、智能工作流和流程自动化
  • 将解决方案从接收和原型阶段推进到测试、生产部署和监控
  • 使用 Python 进行 AI/ML 原型开发、集成、评估和自动化
  • 将 AI 能力与企业应用程序、API、工作流和数据平台连接
  • 创建智能文档工作流,用于分类、提取、摘要、路由及相关用例
  • 评估 AI、生成式 AI 和 LLM 平台的质量、安全性、可靠性、可扩展性、风险和组织适配性
  • 设计试点项目、评估标准、测试计划、成功指标和采用建议
  • 设计负责任的 AI 控制措施,涵盖模型评估、性能监控、人工监督、风险和事件响应
  • 使用 Power Automate 等工具
查看英文原文

AI/ML Engineer — Applied AI & Intelligent Automation

Remote
Active Secret Clearance Required

Build AI That Moves Into Production

Rackner is seeking an AI/ML Engineer to help shape how artificial intelligence is evaluated, built, governed, and deployed across a federal data modernization environment.

This role goes beyond isolated prototypes or model development. You will help determine where AI can create meaningful operational value, whether AI is the right solution, and how promising use cases move from concept into production.

You will have the opportunity to build AI applications, agents, intelligent workflows, and automation while also influencing how emerging AI and LLM technologies are evaluated, governed, monitored, and adopted. Your work may span use-case assessment, solution design, prototyping, testing, deployment, responsible-AI controls, and post-production monitoring.

Working alongside engineering, data, cybersecurity, governance, and mission teams, you will gain visibility across the full AI delivery lifecycle and help shape both the technology and the decisions behind it.

For an engineer who wants to expand beyond a narrow model-development role, this position offers exposure across:

Applied AI engineering → AI/LLM evaluation → intelligent automation → responsible AI → enterprise adoption

You will help turn emerging AI capabilities into practical tools that support real operational needs—not innovation for innovation’s sake.

Then I’d go directly into:

What You’ll Own

  • Turn operational challenges into practical AI use cases, technical requirements, and solution designs
  • Build and deploy AI applications, agents, intelligent workflows, and process automations
  • Take solutions from intake and prototype through testing, production deployment, and monitoring
  • Apply Python to AI/ML prototypes, integrations, evaluations, and automation
  • Connect AI capabilities with enterprise applications, APIs, workflows, and data platforms
  • Create intelligent document workflows for classification, extraction, summarization, routing, and related use cases
  • Evaluate AI, generative AI, and LLM platforms for quality, security, reliability, scalability, risk, and organizational fit
  • Design pilots, evaluation criteria, test plans, success measures, and adoption recommendations
  • Shape responsible-AI controls covering model evaluation, performance monitoring, human oversight, risk, and incident response
  • Apply tools such as Power Automate, Power Apps, Copilot Studio, or comparable intelligent-automation platforms
  • Develop AI-driven analytics capabilities including predictive modeling, forecasting, and natural-language interaction with enterprise data
  • Partner with stakeholders and technical teams to move useful AI capabilities from concept into sustainable operational use

What You'll Bring

  • A track record of building, integrating, evaluating, or deploying AI/ML capabilities in real organizational environments
  • Strong Python skills
  • Hands-on work with generative AI, LLM applications, machine learning, intelligent automation, or related applied-AI technologies
  • Ability to integrate solutions with APIs, enterprise systems, workflows, or data sources
  • Understanding of the AI delivery lifecycle from requirements and prototyping through testing, deployment, monitoring, and iteration
  • Working knowledge of responsible-AI, governance, model evaluation, or AI risk-management practices
  • Technical judgment to assess feasibility, value, implementation complexity, and risk
  • Ability to translate operational needs into practical technical solutions
  • Clear communication across both technical and nontechnical stakeholders
  • Active Secret clearance

Valuable Additional Background

You do not need every item below to be successful in the role.

Background in one or more of these areas would be valuable:

  • Generative AI and enterprise LLM applications
  • AI agents or agentic workflows
  • Power Automate, Power Apps, Copilot Studio, or similar automation platforms
  • Azure AI, Microsoft Fabric, or related Microsoft technologies
  • Intelligent document processing
  • Predictive analytics, forecasting, or natural-language analytics
  • AI governance, human-in-the-loop controls, model monitoring, or evaluation frameworks
  • Organizational AI pilots or technology-selection efforts
  • Responsible AI within DoD or other regulated environments
  • Federal, defense, or education technology environments

Why Rackner

At Rackner, you will work on technology intended for real mission use—not innovation theater.

This role offers the opportunity to help determine where AI creates value, how solutions should be built, and what responsible production adoption looks like within a complex federal environment.

You will collaborate with teams working across AI/ML, cloud, data, DevSecOps, cybersecurity, and modern software engineering while gaining exposure to both technical delivery and the decisions that shape how emerging technology is adopted.

Rackner is a software consultancy building mission-critical systems for the U.S. government. Our teams support federal agencies and national-security missions through modern cloud, software, data, and AI capabilities.

Benefits & Perks

  • Company-supported certifications across AI/ML, cloud, Kubernetes, DevSecOps, security, and related technical areas
  • Clear advancement tracks and future leadership opportunities
  • 401(k) with 100% match up to 6%
  • Medical, dental, vision, life, and disability coverage
  • Generous PTO and paid holidays
  • Home-office equipment and remote-work support
  • Fitness and wellness reimbursement
  • Weekly pay
  • Team events and professional-development opportunities

Equal Opportunity

Rackner is an equal opportunity employer and considers all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or other protected characteristics.

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