Senior AI Engineer
New
J
JobgetherAI infrastructure
Candidates must be based in Europe, including the UKFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Significant senior-level experience
- Required Skills
- PythonGCPGo
Requirements
- Significant senior-level experience building and operating production backend systems using Go, Python, or a similar programming language.
- 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.
- Understand model integration, evaluation, observability, and production failure handling.
- Familiarity with AI coding assistants such as Claude Code, Codex, or Cursor.
- Understand reusable skills, plugins, MCP integrations, agent workflows, and agent orchestration.
- Demonstrate pragmatic technical judgment in architecture, model selection, integrations, and build-versus-buy decisions.
- Bring a security-conscious engineering mindset, including understanding identity, access control, secrets, data exposure, and secure system design.
- Communicate and collaborate effectively with non-technical stakeholders and treat internal users as customers.
- Be based in Europe, including the UK, and have a valid right to work.
Responsibilities
- Design, build, deploy, and operate backend services, APIs, and integrations for internal AI applications and workflows.
- Develop secure deployment pathways that let non-engineering teams launch internal AI tools with appropriate guardrails.
- Build reusable AI capabilities, including skills, plugins, MCP connectors, agent workflows, and a central registry of tools, ownership, and costs.
- Develop shared AI services and standard tooling for employees.
- Evaluate architectures, AI models, platforms, integrations, and build-versus-buy options.
- Help define the technical direction, architecture, and scope of the internal AI platform.
- Establish risk-based review processes and implement technical controls and security safeguards.
- Move business-critical AI automations into reliable, maintainable infrastructure.
- Improve technical standards, runtimes, deployment practices, and engineering practices for internal AI development.
- Drive adoption through demonstrations, documentation, integration support, and collaboration with departmental AI champions.
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