Senior AI Engineer
New
R
refurbedAI tools
This role is open to candidates based in Europe (including UK) onlyFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonGo
Requirements
- Have a strong track record building and operating production systems in Go, Python, or a similar language.
- Be comfortable owning services end to end, from architecture and deployment to secrets management, monitoring, and reliability.
- Have built AI- or LLM-powered applications for production use.
- Understand the AI application lifecycle, including model integration, evaluation, observability, and production incident response.
- Have hands-on experience with AI coding assistants such as Claude Code, Codex, or Cursor.
- Understand reusable skills, plugins, MCP integrations, agent workflows, and agent orchestration.
- Be able to evaluate tools and platforms and make pragmatic build-versus-buy decisions.
- Apply sound technical and product judgment, including providing constructive feedback on AI-assisted applications.
- Work effectively with non-technical stakeholders and treat internal users as customers.
- Take a security-conscious approach to identity, access control, secrets, and data exposure.
- Be based in Europe, including the UK, and have a valid right to work.
Responsibilities
- Design, build, and operate reliable backend services, APIs, and integrations for internal AI tools and workflows.
- Manage a secure, supported deployment path with guardrails for launching internal tools.
- Build and maintain shared AI capabilities, including reusable skills, plugins, MCP connectors, and a central registry of internal tools, owners, and costs.
- Deliver company-wide AI services and a standard toolkit for new joiners.
- Evaluate architecture, models, integrations, and build-versus-buy options.
- Help define a risk-based review framework and assess internally built AI applications against it.
- Implement safeguards for access control, sandboxing, model usage, and data handling.
- Move business-critical automations to reliable, supported infrastructure and improve technical standards and runtimes.
- Drive adoption through demonstrations, documentation, integration support, and collaboration with departmental AI champions.
- Document systems and translate relevant developments in AI models and tools into practical opportunities.
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