Role overview
*Are you excited about experimenting with AI and turning ideas into working solutions? Do you enjoy learning by building, testing, and iterating in a fast-moving R&D environment?
Join the Netline AI R&D team, where you will work on exploring new AI concepts, building proof-of-concepts (POCs), and contributing to minimum viable products (MVPs) that support the future of aviation technology.**
This is a junior-level role with strong growth potential, ideal for someone who wants to deepen their AI/ML engineering skills while working closely with experienced data scientists and engineers.
You will gain hands-on experience with Generative AI, Agentic AI, and traditional machine learning models, contributing to experimental solutions that can later be scaled by our productization team. You’ll be supported by senior team members while gradually taking more ownership of components and experiments as your experience grows.
What you'll work on
- Contribute to the development of AI proof-of-concepts (POCs) under the guidance of senior team members.
Support the transition of successful POCs into MVPs by implementing models, experiments, and prototypes. - Experiment with Generative AI technologies (LLMs, prompt engineering, basic fine-tuning, RAG concepts).
- Participate in exploring hybrid AI approaches that combine ML models with agent-based or rule-based logic.
- Collaborate with data scientists and engineers to integrate models into functional prototypes.
- Document experiments, results, and learnings clearly to support knowledge sharing within the team.
- Stay curious and keep up with emerging AI tools, frameworks, and best practices.
What we're looking for
- Building small AI/ML prototypes, POCs, or university/research projects.
- Retrieval-Augmented Generation (RAG) concepts or agent-based systems.
- Basic MLOps or software engineering practices (Git, CI/CD basics, reproducible experiments).
- Data preparation and exploratory data analysis.
- Any exposure to aviation, transportation, or complex operational datasets.