Mid/Senior AI/ML Engineer with network security @ CodiLime

3 hours ago

Remote, Czech Republic CodiLime Full time
Do we have a match

As a Senior/Mid AI/ML Engineer, you must meet most of the following criteria:

  • Solid AI/ML expertise: strong understanding of ML concepts and hands-on experience with classic ML and modern LLM architectures
  • Ability to go beyond APIs: experience designing data flows, embeddings, RAG pipelines, fine-tuning, or agent-based systems
  • Strong software engineering background (Python, system design, scalable architectures)
  • Experience in networking, infrastructure, or DevOps (containerization, cloud deployment
  • Strong problem-solving mindset, ability to work independently and in a team
  • English at least B2 level, C1/C2 preferred

A perfect match candidate will also possess the following experience:

  • Experience deploying AI/ML solutions in production
  • Experience with data-intensive systems (streaming, graph or network data)
  • Familiarity with MLOps practices and scalable deployment in cloud or hybrid environments
  • Hands-on experience with one of the following domains: computer networks, systems security, cloud-native applications
The project and the team

We're building software for modern platforms and operating systems, supporting leading networking equipment manufacturers, cloud-native solutions, and infrastructure projects. AI/ML is increasingly at the heart of these initiatives—not as an add-on, but as a core tool to address complex engineering and networking challenges. We are seeking an engineer with a strong software engineering background, solid AI/ML expertise, and experience in at least one of the following domains: system security, cloud-native solutions, or computer networks.

,[Building and integrating AI/ML solutions (including LLMs, RAG, and agents) into networking and infrastructure projects, Designing and implementing data pipelines to preprocess, enrich, and pass data into AI/ML models, Developing and refining ML/DL models (PyTorch, Scikit-learn, Transformers, etc.) for practical use cases, including model evaluation, retraining, and optimization, Collaboration with software engineers and DevOps to deliver production-ready solutions, Contribution to technical discussions and proposing innovative ways to apply AI/ML in our technical domains] Requirements: Python, Machine learning, RAG, Networks Tools: . Additionally: Sport subscription, Training budget, Private healthcare, Flat structure, Small teams, International projects, Free coffee, Free beverages, Free lunch, No dress code, Modern office, In-house trainings, Playroom, Bike parking.

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