AI Operations Engineer (Internal Agents & Workflow Automation)
M
M-FilesDocument Management System
United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- Node.jsPythonTypeScriptC#CI/CDRESTful APIsLLM
Requirements
- Demonstrated ability to build and maintain end-to-end software (design → build → deploy → operate)
- Strong engineering fundamentals
- Proficiency in at least one modern programming language (e.g., Python, TypeScript, C#, Node.js)
- Comfort learning new programming languages as needed
- Practical experience integrating systems via APIs, authentication, and structured data formats
- Strong ability to work with non-technical stakeholders: translate ambiguous problems into clear specs, iterate quickly, and drive adoption
- Experience building cloud-based services and surrounding engineering hygiene (CI/CD, source control, test automation, operational monitoring)
- Comfort with secure and scalable platform concepts (networking, identity, secrets, infrastructure automation)
- Experience or strong interest in AI-assisted development as part of daily engineering practice
- Hands-on experience building LLM-powered tools/agents (prompting, tool use, retrieval, evaluation/quality approaches)
- Ability to design safe and predictable AI systems (validation, fallbacks, human-in-the-loop, clear failure handling)
- Familiarity with enterprise security/compliance expectations (access controls, audit trails, change management, data governance) (Preferred)
- Experience modernizing processes (Lean/ops mindset) and designing systems (Preferred)
- Experience building internal tools that drive adoption across multiple functions (Preferred)
Responsibilities
- Partner with functional leaders to identify high-value AI opportunities (internal process focus)
- Map current state processes, identify bottlenecks, and redesign processes for automation readiness
- Define success metrics and translate business goals into a build plan
- Design and implement internal AI agents using modern LLM patterns (tool use, RAG, structured outputs, human-in-the-loop)
- Build whole-product solutions: lightweight UX, service/API layer, integrations, data access, and automation triggers
- Use AI-assisted development techniques to speed delivery while sustaining maintainability and readability
- Own reliability: monitoring, alerting, logging, incident response, and continuous improvements
- Establish repeatable patterns for onboarding new workflows and scaling existing ones
- Implement appropriate guardrails for data minimization, access controls, secrets management, and output validation
- Ensure solutions meet internal security and compliance expectations (audit readiness, change management)
- Coordinate across IT/Security, Legal/Privacy, and functional SMEs for solution approval and adoption
- Communicate progress with crisp updates and manage tradeoffs between speed and rigor
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