AI Agent Engineer

Role overview Make AI agents reliable at real tasks, owning instructions, tool use, evaluations, and model choice for server-side systems that find prospects, verify emails, move deals, book appointments, and send outreach. The work focuses on planning, tool calls, approval flows, and recovery when things go wrong.

Responsibilities - Improve how agents convert plain-language tasks into correct sequences of tool calls. - Build evaluations from real workflows, such as prospecting runs and campaign sends, and track them over time. - Tune instructions and approval behavior so agents pause and ask at exactly the right moments. - Compare Claude, GPT, and Gemini models on cost, speed, and quality for different types of work. - Investigate failed turns end to end and turn findings into fixes and guardrails.

Requirements - 3+ years of software engineering with at least one LLM-powered feature shipped to real users. - Hands-on experience with tool or function calling and structured outputs. - An evaluation mindset: measuring before believing an improvement. - Solid backend fundamentals including APIs, queues, retries, and observability. - Good judgment about when an agent should act and when it should ask.

Nice to have - Experience with token budgeting and cost controls in production. - Background in sales, marketing, or CRM automation. - Contributions to open-source agent or evaluation tooling.

Benefits and work setup - Meaningful equity with a long exercise window. - Remote-first across Europe or Africa, async by default with a few overlap hours. - Private health cover (or a stipend where appropriate), $1,500 yearly learning budget, $1,000 home-office stipend plus a laptop of choice, and flexible time off with a 25-day minimum.