AI Operations Engineer (Internal Agents & Workflow Automation) (Remote, US)

M-Files

3d•Remote

About The Position

M-Files is building a company-wide capability to operationalize AI—turning promising LLM prototypes into secure, reliable, maintainable internal tools that materially improve how teams work. This role sits in Strategic Operations and functions as a hands-on builder: you will partner with business leaders to identify high-value workflows that can be accelerated with AI, help reshape the underlying processes, and then build, deploy, and run the AI-enabled solutions end-to-end. This is not a traditional product software engineer role. Instead, it is an execution-oriented engineering role embedded in the business: you’ll move quickly from problem definition to production, while applying the right risk controls, oversight, security, and compliance expectations required for internal systems at scale. You will leverage AI-assisted development tools as part of your daily workflow to accelerate delivery while maintaining quality.

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.
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).

Nice To Haves

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.

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 compliance.
Map 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 case.
Use 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 improvements.
Establish 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 design
Implement appropriate guardrails: data minimization, access controls, secrets management, safe prompt/tooling patterns, output validation, and traceability.
Ensure 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 adopted.
Communicate progress with crisp updates; manage tradeoffs between speed and rigor.