Senior AI Engineer
New
R
refurbedAI engineering
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
- Bring experience as a senior back-end engineer building and operating production systems in Go, Python, or a similar language.
- Own services end to end, including architecture, deployment, 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.
- Evaluate tools and platforms pragmatically and make build-versus-buy decisions.
- Apply sound technical and product judgment, including assessing what is ready to ship and when to stop or simplify an initiative.
- 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.
- Nice to have: experience with Google Cloud Platform, strong Go or Python experience, product discovery, corporate IT, identity and access management, application security, or supporting an AI transformation.
Responsibilities
- Design, build, and operate reliable back-end services, APIs, and integrations for internal AI tools and workflows.
- Manage a secure, supported deployment path with built-in guardrails for internal tools.
- Build and maintain shared AI capabilities, including reusable skills, plugins, MCP connectors, and a central tool registry.
- Deliver company-wide AI services and a standard toolkit for employees.
- Evaluate architecture, model selection, integrations, and build-versus-buy options.
- Help define a risk-based review framework and assess internally built AI applications.
- Implement access controls, sandboxing, and controls for model usage and data handling.
- Move business-critical automations to reliable, supported infrastructure and improve technical standards and runtimes.
- Drive adoption through demonstrations, documentation, and hands-on integration support.
- Partner with the Head of Internal Technology & AI and departmental AI champions to deliver and support solutions.
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