Key skills
Software development lifecyclePythonTypeScriptC#Node.jsAPI integrationAuthenticationStructured data formatsCloud-based servicesCI/CDSource controlTest automationOperational monitoringNetworkingIdentity managementSecrets managementInfrastructure automationAI-assisted developmentLarge language models (LLMs)Prompt engineeringTool use for AI agentsRetrieval-augmented generationEvaluationquality assurance of AI systemsAI system safety designHuman-in-the-loop systemsEnterprise securitycomplianceAccess controlsAudit trailsChange managementData governanceProcess modernizationLean operationsInternal tools developmentCommunication with non-technical stakeholdersCAILLMSource ControlChange Management
About this role
M-Files is redefining how work gets done with its context-first document management system. The AI Operations Engineer will partner with business leaders to operationalize AI, identifying workflows that can be enhanced with AI, and building, deploying, and maintaining AI-enabled solutions.
Responsibilities:
- Discover & shape high-value AI opportunities (internal process focus) Partner with functional leaders to identify workflows where AI can remove friction, reduce cycle time, improve accuracy, or strengthen complianceMap the current state process, identify bottlenecks and failure modes, and re-design the process to be automation-ready (clarify inputs/outputs, decision points, data sources, controls, and ownership)Define success metrics (time saved, error reduction, throughput, adoption, auditability) and translate business goals into a build plan
- Build internal AI agents and automation tools (end-to-end ownership) Design and implement internal agents using modern LLM patterns (tool use/function calling, retrieval-augmented generation where needed, structured outputs, and human-in-the-loop checkpoints)Build whole-product solutions: lightweight UX, service/API layer, integrations, data access, and automation triggers—appropriate to the use caseUse AI-assisted development techniques to speed delivery while sustaining maintainability and readability
- Operate, mantain, and scale (production mindset) Own reliability: monitoring, alerting, logging, incident response, and continuous improvementsEstablish repeatable patterns for onboarding new workflows and scaling existing ones (templates, shared components, evaluation harnesses, documentation)Create and maintain runbooks and lightweight training so internal teams can adopt solutions confidently
- Risk, control, oversight, security & compliance by designImplement appropriate guardrails: data minimization, access controls, secrets management, safe prompt/tooling patterns, output validation, and traceabilityEnsure solutions meet internal security and compliance expectations (including audit readiness, change management discipline, and clear ownership)Maintain clear documentation of how systems work, what data they touch, and how risks are mitigated
- Cross-functional coordination Coordinate across IT/Security, Legal/Privacy, and functional SMEs to get solutions approved and adoptedCommunicate progress with crisp updates; manage tradeoffs between speed and rigor
- Outcomes to be achieved A portfolio of high-impact internal AI agents deployed into real workflows (not demos), with measurable business outcomesA scalable operating model for internal AI: reusable components, clear governance, and a predictable path from idea → productionReduced process friction through AI + process redesign, not AI bolted onto broken workflowsHigh trust in outputs through appropriate controls, auditability, and operational reliability
Requirements:
- Demonstrated ability to build and maintain end-to-end software (design → build → deploy → operate), with strong engineering fundamentals
- Proficiency in at least one modern programming language (e.g., Python, TypeScript, C#, Node.js) and comfort learning what's 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 the surrounding engineering hygiene (CI/CD, source control, test automation, and 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 LLMs/agent capabilities (expected for this role)
- Hands-on experience building LLM-powered tools/agents (prompting, tool use, retrieval where appropriate, and evaluation/quality approaches)
- Ability to design safe and predictable AI systems (validation, fallbacks, human-in-the-loop, and clear failure handling)
- Familiarity with enterprise security/compliance expectations (access controls, audit trails, change management, data governance)
- Experience modernizing processes (Lean/ops mindset) and designing systems that align to how teams actually work
- Experience building internal tools that drive adoption across multiple functions