Remote LLM Engineer — RAG & Production AI Solutions

Umanova SA

Genf

Remote

CHF 66,000 - 113,000

Full time

14 days+
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Job summary

Umanova SA is seeking a remote Mid-Level AI Engineer to develop production‑ready generative AI solutions using LLMs, RAG, and cloud platforms. The role emphasizes practical deployment over research and collaborates with software engineers and product teams across Europe.

You will build and integrate AI tools, optimize models for real‑world use, and ensure security and compliance while delivering value to business workflows.

Qualifications

  • 1-3 years of applied software, data, or ML engineering experience.
  • Strong Python skills and ML framework experience (TensorFlow, PyTorch, scikit-learn).
  • Familiarity with cloud integrations (AWS, GCP, Azure).
  • Interest in LLMs, RAG, prompt engineering, and applied AI projects.
  • Proven ability to ship production-grade AI solutions.

Responsibilities

  • Model customization and RAG to tailor LLMs for business use.
  • API & platform integration using AWS Bedrock, OpenAI, or similar APIs.
  • Develop AI-powered tools to enhance operations and decision-making.
  • Collaborate with data engineers to prep data for models and ensure security/compliance.
  • Monitor and refine deployments for scalability and reliability.
  • Document work and communicate technical concepts to diverse audiences.

Skills

Python
ML frameworks
Communication
Cross-functional collaboration

Education

Bachelor's or Master's in Computer Science, Engineering, Data Science

Tools

AWS Bedrock
OpenAI API
LangChain
Docker
CI/CD

Job description

Umanova SA is seeking a remote Mid-Level AI Engineer to develop production‑ready generative AI solutions using LLMs, RAG, and cloud platforms. The role emphasizes practical deployment over research and collaborates with software engineers and product teams across Europe.

You will build and integrate AI tools, optimize models for real‑world use, and ensure security and compliance while delivering value to business workflows.

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