• Design, build, deploy, and operate reliable backend services, APIs, and integrations supporting internal AI applications and workflows.

• Develop secure and supported deployment pathways that allow non-engineering teams to launch internal AI tools with appropriate guardrails.

• Build shared AI capabilities, including reusable skills, plugins, MCP connectors, agent workflows, and a central registry covering internal tools, ownership, and costs.

• Develop company-wide AI services and standard tooling that help employees adopt AI effectively in their day-to-day work.

• Evaluate architecture, AI models, platforms, integrations, and build-versus-buy options using pragmatic technical and product judgment.

• Help define the technical direction, architecture, and scope of the internal AI platform.

• Establish risk-based review processes for internally developed AI applications and translate governance requirements into practical technical controls.

• Implement security safeguards covering identity, access control, sandboxing, model usage, secrets, and data handling.

• Move business-critical AI automations from individual machines and unsupported environments into reliable, maintainable infrastructure.

• Establish and continuously improve technical standards, runtimes, deployment practices, and engineering best practices for internal AI development.

• Partner with technology leadership and core engineering teams to turn AI strategy into scalable technical solutions.

• Drive adoption through demonstrations, documentation, integration support, and collaboration with departmental AI champions.

• Help teams overcome technical blockers and turn AI ideas into safe, maintainable, production-ready solutions.

• Maintain clear documentation so systems can be operated, maintained, and extended by other team members.

• Monitor developments in AI models, tools, coding assistants, and engineering practices, translating relevant advances into practical business opportunities.

Requirements:

• Significant senior-level experience building and operating production backend systems using Go, Python, or a similar programming language.

• Demonstrated experience owning services end to end, including architecture, deployment, secrets management, monitoring, reliability, and ongoing operations.

• Hands-on experience building AI- or LLM-powered applications for production use, with an understanding of model integration, evaluation, observability, and production failure handling.

• Familiarity with modern AI development tools and ecosystems, including AI coding assistants such as Claude Code, Codex, or Cursor.

• Understanding of emerging AI engineering concepts such as reusable skills, plugins, MCP integrations, agent workflows, and agent orchestration.

• Strong ability to evaluate technical options pragmatically and make sound decisions around architecture, model selection, integrations, and build-versus-buy.

• Excellent technical and product judgment, including the ability to simplify initiatives, challenge assumptions, or recommend against building when appropriate.

• Strong communication and collaboration skills, with the ability to work effectively with non-technical stakeholders and treat internal users as customers.

• A security-conscious engineering mindset, with an understanding of identity, access control, secrets, data exposure, and secure system design.

• Strong ownership, autonomy, curiosity, and willingness to experiment, learn from failure, and continuously improve.

• Google Cloud Platform experience is a plus.

• Strong Go experience, or strong Python experience combined with an interest in working with Go, is advantageous.

• Experience with product discovery, corporate IT, identity and access management, or application security is a plus.

• Experience supporting an organisation through an AI transformation is advantageous.

• Candidates must be based in Europe, including the UK, and have a valid right to work.

Benefits:

• Fully remote-first working environment with the flexibility to work from your preferred location.

• Option to work from a well-equipped office in Vienna, including a dog-friendly environment.

• Flexible working hours and autonomy to organise your schedule around your work and personal commitments.

• Dedicated learning budget to support courses, conferences, and other professional development opportunities.

• Access to private sessions with experts supporting personal growth and mental wellbeing.

• Regular internal workshops focused on skills development, knowledge sharing, and cross-functional collaboration.

• Company-wide office weeks held three times per year, combining in-person collaboration, workshops, and team-building activities.

• Opportunity to work on high-impact AI initiatives and help shape the organisation’s transition toward becoming AI-native.

• Culture focused on ownership, open feedback, experimentation, knowledge sharing, and fact-based decision-making.

• Inclusive and collaborative international working environment.

• Opportunity to take on significant technical ownership while working closely with senior technology leadership and engineering teams.