Artificial Intelligence Engineer

thegiglab; Amsterdam; On-site; EUR 90,000 - 150,000; Full time

Job summary

A leading AI technology firm is seeking an Agentic AI Engineer to design and deploy autonomous AI systems that exceed traditional capabilities. The ideal candidate will engineer solutions that include behavior orchestration and multi-step planning, utilizing tools and integration with APIs and databases. Strong programming skills in Python and experience with AI frameworks such as LangChain are essential. This position entails working within a dynamic environment focused on innovative AI technologies in Amsterdam.

Qualifications

• Strong proficiency in Python is essential.
• Deep understanding of OpenAI APIs and function calling.
• 2-5 years in software engineering, focused on LLM/agentic systems.

Responsibilities

• Design Agentic Architectures for multi-step planners.
• Integrate LLMs with external tools for autonomous actions.
• Build systems for specialized agent collaboration.
• Build production deployment frameworks with evaluation for reliability and latency.
• Collaborate in multi-agent systems for complex tasks.

Skills

Strong proficiency in Python

Understanding of OpenAI APIs

Experience with Docker

Hands-on experience with LangChain

Familiarity with TypeScript/Node.js

Knowledge of vector databases

REST APIs

SQL

CI/CD

2–5+ years software experience

Tools

Docker

Kubernetes

LangGraph

AutoGen

OpenAI API

Claude

Llama

Pinecone

Weaviate

Job description

An Agentic AI Engineer designs, builds, and deploys autonomous AI systems that do not just generate text (like traditional chatbots) but rather reason, plan, use tools, and take actions to achieve multi-step goals with minimal human oversight. This role focuses on behavior, tool-use orchestration, and workflow state management.

Key Responsibilities

• Design Agentic Architectures: Implement multi-step planners, ReAct loops, state machines, and long-term memory systems (e.g., LangGraph, AutoGen).
• Tool-Use Orchestration: Integrate LLMs with external tools, APIs, databases, and enterprise systems to enable autonomous actions.
• RAG & Context Management: Develop Retrieval-Augmented Generation (RAG) pipelines and manage agent context to ground decisions in real-time data.
• Production Deployment: Create evaluation frameworks for reliability, accuracy, cost, and latency, including guardrails and human-in-the-loop controls.
• Multi-Agent Systems: Build systems where specialized agents collaborate to complete complex tasks.

Required Skills & Qualifications

• Programming: Strong proficiency in Python (essential) and familiarity with TypeScript/Node.js.
• AI Frameworks: Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or similar.
• LLMs & Tools: Deep understanding of OpenAI APIs, Claude, Llama, and function calling.
• Data & Backend: Proficiency with vector databases (Pinecone, Weaviate), RAG, REST APIs, and SQL.
• DevOps: Experience with Docker, Kubernetes, and CI/CD pipelines.
• Experience: Generally 2–5+ years in software engineering, with 1-2 years focused on LLM/agentic systems.

Core Differences from Traditional AI Roles

• Behavior > Prediction: Focuses on what the agent does (action) rather than just what it predicts.
• Autonomous End-to-End: Shifts from one-shot prompting to persistent, goal-driven agents that maintain state.
• Tool Integration: Focuses heavily on enabling AI to interact with outside software (APIs/Databases).

Example Job Titles

• Senior Agentic AI Engineer
• Principal AI Engineer – Agentic Systems
• Agentic AI Developer
• Generative AI / Agentic AI Senior Engineer