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Wallet icon Coin icon Rate 6 800 € - 7 400 € / month info
Timer icon Form of cooperation Full-time
Briefcase icon Sector Logistics
Location icon Location 100% Remote

info The reward is calculated upon delivery of 20 MD per month (1MD=8h)

Project duration 6 months with the possibility of extension
Period of cooperation 02.01.2026 - 01.07.2026
Start date 02.01.2026 - 05.01.2026
Technology
  • MS Azure
  • Flask
  • Python
  • Django
  • GCP Google Cloud Platform
  • LLM Large Language Model
  • FastAPI
Languages
  • English flag English - active, B2/C1/C2

Project description

  • Participate in the development of an innovative AI platform enabling generative AI models and their safe and scalable deployment into operation
  • Collaboration in a multicultural team that connects domain experts, data science, ML engineering and cloud infrastructure to support internal and customer LLM solutions
  • Integration and optimization of GenAI models and proxy servers to ensure high performance and reliability
  • Cooperation with AI engineers and data scientists on the design and operation of API interfaces, deployment pipelines and model monitoring
  • Troubleshooting technical problems and tuning the performance of GenAI models and their dependencies
  • Development and management of backend infrastructure (preferably in Python and JavaScript / Node.js) and REST/GraphQL APIs
  • Collaboration in agile development and communication with internal teams to ensure solutions comply with business requirements
  • The possibility to work full remote across the entire EU

Project requirements

  • Advanced experience with:
    • Backend development in Python
    • Deploying and operating GenAI models and their optimization
    • Designing and operating API interfaces and web frameworks (e.g. FastAPI, Flask)
    • Public cloud platforms like Azure or GCP
    • Operating GenAI proxies (e.g. VLLM, LM Studio, etc.)
  • Experience with:
    • Programming in JavaScript / Node.js
    • Collaborating with data science and ML engineering teams
    • Tuning LLM performance and troubleshooting their deployment
    • Practical application of prompt engineering and model fine-tuning
    • Communication and collaboration in a multicultural team
  • Advanced knowledge of:
    • Principles of machine learning, neural networks and the functioning of LLMs
    • Deploying models in a cloud environment and DevOps pipelines
  • Knowledge of:
    • Tools like Docker, Git, CI/CD, logging and monitoring (e.g. Prometheus, Grafana)
    • Agile development methodologies and SDLC
  • Advantage:
    • Knowledge of other Python frameworks (e.g. Django)
    • Experience with API security and access models (OAuth, OpenID)
    • Orientation in the field of NLP, NLU, embeddings or retrieval-augmented generation
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