AI Engineer, Developer Automation

Mbianchi; Amsterdam; Híbrido; EUR 175 000 - 245 000; Tempo integral

Resumo da oferta

Mbianchi is seeking engineers to own the development of agentic systems that automate real workflows. You will build agents using APIs, MCP servers, and governed data sources, with a focus on safety, observability, and measurable value.

You will work with Python or TypeScript, integrate production APIs, and evaluate model quality, latency, and costs while ensuring proper data handling and human oversight where needed.

Qualificações

• Strong Python or TypeScript software engineering.
• Hands-on work with production APIs and data systems.
• Experience with LLM APIs, agent SDKs, and tool calling.

Responsabilidades

• Own end-to-end automation of workflows with probabilistic components.
• Build agents/assistants using APIs, MCP servers and governance data sources.
• Create evaluation datasets, quality gates, tracing and observability for AI systems.
• Design permission boundaries, data protection and human approval for actions.
• Collaborate with users and domain experts to ship, measure, and improve workflows.

Conhecimentos

Python

TypeScript

API integration

production systems

Ferramentas

LLM APIs

Agent SDKs

MCP

Tool calling

Descrição da oferta de emprego

Agents, assistants, MCP integrations, and automation for jobs people already do. No chatbot looking for a reason to exist.

The mandate.

Own the outcome, stay close to the implementation, and improve the system around the work.

Build agentic systems around real workflows, tools, and data, with measurable value, explicit human ownership, and enough evaluation to know when the system is useful or wrong.

This is software engineering with probabilistic components, not a prompt‑writing role. Security, observability, failure handling, and product adoption remain first‑class concerns.

Impact Material

Agency High

Management Context, then space

Bureaucracy Low

What this role owns.

• OWN-01 Map repeated work before automating it, then choose where deterministic software, LLMs, or human judgement should own each step.
• OWN-02 Build agents and assistants that use APIs, MCP servers, internal tools, and governed data sources safely.
• OWN-03 Create evaluation datasets, quality gates, tracing, feedback loops, and production observability for model behaviour.
• OWN-04 Design permission boundaries, auditability, data protection, and human approval for consequential actions.
• OWN-05 Work with users and domain experts to ship, measure, and continuously improve the complete workflow.

What this needs.

• Strong Python or TypeScript software engineering and experience integrating production APIs and data systems.
• Hands-on work with LLM APIs, agent SDKs, tool calling, retrieval, MCP, and structured outputs.
• Experience evaluating model quality, latency, reliability, cost, and failure modes beyond demo prompts.
• A practical understanding of identity, permissions, sensitive data, and human-in-the-loop control.
• Product judgement: the ability to reject AI when a script, form, search index, or workflow change is better.

What success looks like.

• The system removes measurable work without hiding ownership or creating a new review bottleneck.
• Tool use is scoped, auditable, and recoverable when models or dependencies fail.
• Evaluation and telemetry catch regressions before users have to explain that the assistant became worse.
• Users adopt the workflow because it fits their job, not because the interface says AI.

Top-quartile ranges for this scope. Ranges are base salary; equity and bonus may be additional. The cash/stock split is negotiable.

EMEA €175k–€245k Base salary Top-quartile EMEA target

US remote $235k–$300k Base salary Remote US target

US major hubs $290k–$350k Base salary San Francisco, Seattle, New York, and comparable markets

Who you need to be

• At least 7+ years of software engineering experience, including hands-on work with production AI or agentic systems.
• Built agents and assistants connected to real tools, APIs, workflows, and governed data.
• Strong Python or TypeScript skills with experience in LLM APIs, MCP, agent SDKs, and structured tool calling.
• Implemented evaluations, tracing, observability, security boundaries, and human ownership for AI workflows.
• Created automation that removed measurable developer, field, or operational toil.

Looks compatible.