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What you’ll work on

Full posting
  • Build backend services and REST APIs in Python and FastAPI

  • Implement LangGraph and LangChain agent workflows with human-in-the-loop steps

  • Build document ingestion and retrieval (RAG) pipelines with citations

  • Integrate LLMs from Azure OpenAI, OpenAI, Mistral, or IBM watsonx

  • Write automated tests and evaluate retrieval and agent behaviour

From the employer’s posting
Build backend services and REST APIs using Python and FastAPI
Implement LangGraph and LangChain workflows covering state management, routing, tools, memory, retries, and human-in-the-loop interactions.
Build document ingestion and retrieval pipelines covering extraction, chunking, embeddings, vector databases, hybrid search, reranking, and source citations.
Integrate models through Azure OpenAI, OpenAI, Mistral, or IBM watsonx
Write automated tests and evaluate retrieval quality, generated responses, and agent behaviour.
Apply at FUJITSU

Posting record

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Actively hiringFirst seen by us 3 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 10, 20263 days ago
Recorded sightings
4
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 3

Early. Similar jobs usually come down between day 4 and day 77.

At FUJITSU, jobs usually come down by about day 21.

From 51 similar postings that came down, and 12 of FUJITSU’s. Coming down is not always a hire.

FUJITSU is hiring

13 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
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See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • Azure
  • Docker
  • FastAPI
Where the posting names them
MY RESPONSIBILITIES : - AI Application Development: Build backend services and REST APIs using Python and FastAPI, with asynchronous processing, robust error handling, and integration with business systems. - Agentic Workflows and Orchestration: Implement LangGraph and LangChain workflows covering state management, routing, tools, memory, retries, and human-in-the-loop interactions.
Required technical skills : - Strong Python and asynchronous programming: Practical experience with async/await, asyncio, concurrent API calls, timeouts, retries, cancellation, and task management. - FastAPI and REST APIs: API design, input validation, authentication, and consistent error handling.
- Vibe coding and AI-assisted development: Practical experience with tools such as Claude Code, GitHub Copilot, or Codex, including providing clear instructions, reviewing generated changes, and validating results through tests. - Software engineering: Git, Docker, CI/CD, and Python testing with pytest, including unit, integration, asynchronous tests, and mocks. - Cloud: Experience deploying applications to a cloud platform, ideally Microsoft Azure.
- Agentic Workflows and Orchestration: Implement LangGraph and LangChain workflows covering state management, routing, tools, memory, retries, and human-in-the-loop interactions. - LLM Integration: Integrate models through Azure OpenAI, OpenAI, Mistral, or IBM watsonx, managing structured outputs, latency, costs, and service failures. - RAG Development: Build document ingestion and retrieval pipelines covering extraction, chunking, embeddings, vector databases, hybrid search, reranking, and source citations.
- LangChain: Tool integration, prompting, structured outputs, and LLM integration. - LLM integration: Hands-on experience with Azure OpenAI/OpenAI or equivalent providers, including rate limits, latency, and cost management. - RAG systems: Experience with ingestion, chunking, embeddings, vector databases, hybrid search, reranking, citations, and retrieval evaluation.
- Software engineering: Git, Docker, CI/CD, and Python testing with pytest, including unit, integration, asynchronous tests, and mocks. - Cloud: Experience deploying applications to a cloud platform, ideally Microsoft Azure. - AI governance and regulatory awareness: Familiarity with the EU AI Act and GDPR, including their practical implications for data privacy, human oversight, transparency, traceability, and responsible AI development.
The following constitute an advantage: - Microsoft AI certifications: Particularly those focused on Azure AI, generative AI, and agentic solutions. - Claude certifications: Particularly those focused on architecture, Claude Code, and agent development.
- Quality and Evaluation: Write automated tests and evaluate retrieval quality, generated responses, and agent behaviour. Use LangSmith or equivalent tools to trace workflows and investigate failures. - Production Engineering: Package applications with Docker, contribute to CI/CD pipelines, and support cloud deployment. Apply secure development practices, including authentication, access control, and secrets management. - Rapid Prototyping and AI-Assisted Development: Use vibe coding and AI-assisted development tools to accelerate prototyping and implementation, taking responsibility for reviewing, testing, securing, and maintaining the resulting code.
- Strong Python and asynchronous programming: Practical experience with async/await, asyncio, concurrent API calls, timeouts, retries, cancellation, and task management. - FastAPI and REST APIs: API design, input validation, authentication, and consistent error handling. - LangGraph: States, nodes, routing, persistence, memory, failure recovery, and human-in-the-loop workflows.

Your next step

Complete your application on www.jobs.global.fujitsu.com. The employer’s form will show what is required.

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Has FUJITSU posted this job before?

Yes. We’ve followed one other Data Science job there since November 2026. It is still up.

  1. LuxembourgStill up after 3 days, first seen Nov 10
  2. This jobUp 3 days so far, first seen Nov 10

Demand for AI Engineer roles

New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.

Weekly new AI Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 7, the most 65, and the latest week had 65.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under AI Engineer, by the work the posting describes.

AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.

Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.

An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.

Job description

View original posting ↗

At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age.

With approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs).

 


MY MISSION : 

As an AI Engineer, your mission is to design, build, and deploy AI-powered applications that solve concrete business problems. Working with the AI Services Team Lead, architects, and business analysts, you will translate business requirements into reliable solutions using large language models, RAG, and agentic workflows.

You will contribute throughout the development lifecycle, from rapid prototyping and technical validation to production deployment and continuous improvement.

MY RESPONSIBILITIES : 
  • AI Application Development: Build backend services and REST APIs using Python and FastAPI, with asynchronous processing, robust error handling, and integration with business systems.
  • Agentic Workflows and Orchestration: Implement LangGraph and LangChain workflows covering state management, routing, tools, memory, retries, and human-in-the-loop interactions.
  • LLM Integration: Integrate models through Azure OpenAI, OpenAI, Mistral, or IBM watsonx, managing structured outputs, latency, costs, and service failures.
  • RAG Development: Build document ingestion and retrieval pipelines covering extraction, chunking, embeddings, vector databases, hybrid search, reranking, and source citations.
  • Quality and Evaluation: Write automated tests and evaluate retrieval quality, generated responses, and agent behaviour. Use LangSmith or equivalent tools to trace workflows and investigate failures.
  • Production Engineering: Package applications with Docker, contribute to CI/CD pipelines, and support cloud deployment. Apply secure development practices, including authentication, access control, and secrets management.
  • Rapid Prototyping and AI-Assisted Development: Use vibe coding and AI-assisted development tools to accelerate prototyping and implementation, taking responsibility for reviewing, testing, securing, and maintaining the resulting code.
  • Responsible AI: Implement privacy, transparency, traceability, and human oversight measures in line with project requirements, working with security and compliance specialists.
  • Collaboration: Document technical decisions and contribute to solution estimates, feasibility assessments, and demonstrations.

Required technical skills :

  • Strong Python and asynchronous programming: Practical experience with async/await, asyncio, concurrent API calls, timeouts, retries, cancellation, and task management.
  • FastAPI and REST APIs: API design, input validation, authentication, and consistent error handling.
  • LangGraph: States, nodes, routing, persistence, memory, failure recovery, and human-in-the-loop workflows.
  • LangChain: Tool integration, prompting, structured outputs, and LLM integration.
  • LLM integration: Hands-on experience with Azure OpenAI/OpenAI or equivalent providers, including rate limits, latency, and cost management.
  • RAG systems: Experience with ingestion, chunking, embeddings, vector databases, hybrid search, reranking, citations, and retrieval evaluation.
  • Multi-agent architecture: Experience implementing agent coordination, delegation, shared context, and failure handling.
  • MCP (Model Context Protocol): Hands-on experience connecting agents to business tools and implementing or integrating MCP clients and servers.
  • A2A (Agent2Agent Protocol): Hands-on experience integrating external agents, exchanging messages, and managing task lifecycles and errors.
  • Observability and evaluation: Experience with LangSmith or equivalent tools for tracing and assessing LLM applications and agent workflows.
  • Vibe coding and AI-assisted development: Practical experience with tools such as Claude Code, GitHub Copilot, or Codex, including providing clear instructions, reviewing generated changes, and validating results through tests.
  • Software engineering: Git, Docker, CI/CD, and Python testing with pytest, including unit, integration, asynchronous tests, and mocks.
  • Cloud: Experience deploying applications to a cloud platform, ideally Microsoft Azure.
  • AI governance and regulatory awareness: Familiarity with the EU AI Act and GDPR, including their practical implications for data privacy, human oversight, transparency, traceability, and responsible AI development.

CERTIFICATIONS

The following constitute an advantage:

  • Microsoft AI certifications: Particularly those focused on Azure AI, generative AI, and agentic solutions.
  • Claude certifications: Particularly those focused on architecture, Claude Code, and agent development.
  • Other relevant certifications: Cloud engineering, AI engineering, AI governance, or multi-agent architecture.
SOFT SKILLS :
  • Ability to translate business requirements into clear technical tasks.
  • Autonomy, reliability, and ownership of development work.
  • Clear communication with engineers, architects, and business analysts.
  • A practical approach to problem-solving, with attention to quality and maintainability.
  • Willingness to learn, share knowledge, and adapt as AI technologies evolve.
LANGUAGES : 
  • French and English: Professional proficiency in both languages, written and spoken, to collaborate with clients and colleagues, participate in technical discussions, and produce clear documentation.


At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.
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Location & working pattern

Luxembourg, LU

- LLM Integration: Integrate models through Azure OpenAI, OpenAI, Mistral, or IBM watsonx, managing structured outputs, latency, costs, and service failures. - RAG Development: Build document ingestion and retrieval pipelines covering extraction, chunking, embeddings, vector databases, hybrid search, reranking, and source citations. - Quality and Evaluation: Write automated tests and evaluate retrieval quality, generated responses, and agent behaviour. Use LangSmith or equivalent tools to trace workflows and investigate failures.
More source context
- LLM integration: Hands-on experience with Azure OpenAI/OpenAI or equivalent providers, including rate limits, latency, and cost management. - RAG systems: Experience with ingestion, chunking, embeddings, vector databases, hybrid search, reranking, citations, and retrieval evaluation. - Multi-agent architecture: Experience implementing agent coordination, delegation, shared context, and failure handling.
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