Internal AI Solution Development Engineer

Summary of the Role

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 within the Internal AI Solution Development team and functions as a hands-on builder, partnering with business leaders across Sales, Marketing, Revenue Operations, Customer Success, and other business functions to identify high-value workflows that can be accelerated with AI, reshape the underlying processes where needed, and build, deploy, and operate AI-enabled solutions end-to-end.

This is not a traditional product software engineering role, nor is it a RevOps analytics role. Instead, it is an execution-oriented AI engineering role embedded in the business. You’ll move quickly from problem definition to production, building intelligent agents and workflow automations while applying the appropriate security, governance, risk controls, 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, reliability, and long-term maintainability.

Success in this role comes from understanding how people work, redesigning processes where appropriate, and building AI-powered solutions that teams genuinely adopt, not simply delivering technical prototypes.

What You Will Be Doing / Responsibilities and Duties

Discover & Shape High-Value AI Opportunities

• Partner with functional leaders across Sales, Marketing, Revenue Operations, Customer Success, and other business functions 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 redesign the process to be automation-ready by clarifying inputs, outputs, decision points, data sources, controls, and ownership.
• Define success metrics such as time saved, error reduction, throughput, adoption, and auditability, and translate business goals into a clear build plan.

Build Internal AI Agents & Automation Tools (End-to-End Ownership)

• Design and implement internal agents and agentic workflows using modern LLM patterns, including tool use/function calling, retrieval-augmented generation where needed, structured outputs, orchestration, and human-in-the-loop checkpoints.
• Build whole-product solutions including lightweight UX, service/API layers, integrations, data access, automation triggers, and connections into business systems such as CRM, marketing automation, customer success platforms, and other internal applications as appropriate to the use case.
• Integrate systems through APIs, authentication, structured data formats, and webhooks to enable agents to reliably read data and trigger actions.
• Use AI-assisted development techniques to speed delivery while sustaining maintainability, readability, and quality.

Operate, Maintain & Scale (Production Mindset)

• Own reliability, including monitoring, alerting, logging, incident response, and continuous improvement.
• Establish repeatable patterns for onboarding new workflows and scaling existing ones through templates, shared components, evaluation harnesses, and documentation.
• Create and maintain runbooks and lightweight training so internal teams can confidently adopt and use solutions.

Risk, Control, Oversight, Security & Compliance by Design

• Implement appropriate guardrails including data minimization, access controls, secrets management, safe prompt and tooling patterns, output validation, and traceability.
• Ensure solutions meet internal security and compliance expectations, including audit readiness, change management discipline, clear ownership, and relevant data privacy and consent requirements.
• Maintain clear documentation of how systems work, what data they access, and how associated risks are mitigated.

Cross-Functional Coordination

• Coordinate across Business Technology, IT/Security, Legal/Privacy, and functional subject matter experts to design, approve, deploy, and drive adoption of AI-enabled solutions across the business.
• Communicate progress through clear, concise updates and manage tradeoffs between speed and rigor.

Outcomes to Be Achieved

• A portfolio of high-impact internal AI agents deployed into real business workflows, including Sales, Marketing, Revenue Operations, and Customer Success, with measurable business outcomes.
• A scalable operating model for internal AI, including reusable components, clear governance, and a predictable path from idea to production.
• Reduced process friction through AI and thoughtful process redesign, not AI bolted onto broken workflows.
• High trust in outputs through appropriate controls, auditability, and operational reliability.