The BioxAI Solution Engineer acts as the bridge between Phagos’ AI research and its biological applications.
This role ensures that AI models and computational tools are seamlessly integrated into Phagos’ R&D and operational workflows.
The engineer translates technical outputs into actionable insights, empowering teams to leverage AI solutions efficiently and effectively.

Main Responsibilities

• Serve as the link between AI researchers, ML engineers, and biology teams to ensure smooth communication and workflow alignment.

• Deploy, test, and maintain AI-driven tools used in bacteriophage–host prediction and related applications.

• Integrate and adapt AI solutions within laboratory and data environments.

• Support experimental validation by connecting computational outputs with biological results.

• Provide documentation, user training, and technical support for AI tools used internally.

• Monitor system performance and propose improvements to enhance reliability and scalability.

• Contribute to the definition and implementation of new features in collaboration with Data & AI teams.

• Strong programming skills in Python and experience with data analysis libraries (NumPy, pandas, scikit-learn).

• Understanding of machine learning principles and experience using frameworks such as PyTorch or TensorFlow.

• Familiarity with biological data (genomics, proteomics, microbiology) and data integration techniques.

• Experience with APIs, databases, or cloud environments (AWS, Docker, Kubernetes) is an advantage.

• Ability to translate technical models into user-oriented solutions and interfaces.

• Excellent communication and teamwork skills, especially in multidisciplinary environments.

• Analytical mindset and ability to solve complex technical challenges.

• Adaptability and curiosity for working at the intersection of AI and biology.

• Strong attention to detail and commitment to reproducible, high-quality work.

• Fluent in English (written and spoken).

• Interest in biotechnology and AI-driven innovation.

Education & Experience

• Master’s degree or PhD in Computational Biology, Bioinformatics, Computer Science, or related field.

• 1–3 years of experience in a similar interdisciplinary role, or strong academic background in applied AI/biology projects.