AI Specialist (m/f/d) at freshcells systems engineering GmbH
40223 Düsseldorf, , Germany -
Full Time


Start Date

Immediate

Expiry Date

01 Dec, 25

Salary

0.0

Posted On

02 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Git, Docker, Web Technologies, Kubernetes

Industry

Computer Software/Engineering

Description

OVERVIEW

We’re seeking an Applied AI Specialist to join our AI team. This role focuses on applying AI models like LLMs, vision OCR, and generative AI to build practical tools such as chatbots, document search systems, and automations. You’ll work hands-on with technologies like Retrieval-Augmented Generation (RAG) to create a knowledge base AI and integrate AI into our workflows via APIs and web interfaces. Beyond building, you’ll teach and present to diverse groups of developers, data scientists, management, and students making good communication skills a must. The role blends on-premises and cloud environments, requiring familiarity with Docker, Kubernetes (k8s), and CI/CD pipelines. If you’re pragmatic, technical, and ready to prototype MVPs, this is for you.

REQUIRED SKILLS

  • Technical Skills:
  • Strong (Python) programming skills.
  • Hands-on experience with RAG, embeddings, and LLM context management.
  • Familiarity with LangChain, LlamaIndex, PocketFlow, or similar AI frameworks.
  • Knowledge of web technologies and networking basics.
  • Proficiency with Docker, Kubernetes (k8s), Git, and CI/CD pipelines (Nix as an alternative is a plus).
  • Ability to present complex ideas clearly to technical and non-technical audiences.
  • Practical mindset for prototyping and scaling AI solutions.

How To Apply:

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Responsibilities
  • Use existing LLMs, vision OCR, and generative AI to solve real-world problems in software engineering and for users of web applications.
  • Create web chatbots, automations, pagebuilder prompt chatbots, APIs and potentially plugins.
  • RAG for knowledge base AI, document search, and programming integrations.
  • Develop Minimum Viable Products (MVPs), then scale solutions, build robust architecture and delegate tasks as needed.
  • Knowledge transfer AI tool usage and concepts.
  • Manage AI solutions in both on-premises and cloud environments.
  • Use and create Model Context Protocol (MCP) servers or similar techniques to enhance AI capabilities.
  • Understand AI risks and opportunities within software engineering and business.
  • Stay up to date on latest models and ways of using AI.
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