[Hiring] Agentic AI Engineer @CVEDIA

Role Description

We are hiring an engineer who builds systems out of AI agents and knows enough about training neural networks to point those agents at the right problems. You will start by learning our automated model pipeline, and then take on your first model. After that, the work widens to new research, new customer problems, and our software. You will design the harness, and the agents will do much of the work.

What you will do

• Learn, maintain, and improve our synthetic data pipeline and automated training system.
• Take over a model as your first project, and improve it with an agent loop that generates its own data as it goes.
• Build closed loops in which agents train detectors and classifiers, test them against validation sets, and improve them with little human input.
• Design systems that handle false positives from the field, flagging only uncertain detections and fixing the rest automatically.
• Lead our research on vision-language models, fine-tuning them for specific tasks and using agents to track the latest techniques.
• Solve new detection and classification problems for customers and partners by building new models or extending existing ones.
• Apply agents beyond models, for example to QA automation and to code bases that find and fix their own bugs.
• Share what works with the team, and present results to customers when a project needs it.

Qualifications

• You have built your own agent harnesses or agentic workflows, and you can show us the work.
• You have run many agents at once, and you have dealt with the usual problems: communication between agents, token use, and checking that tasks are really finished.
• You have a background in professional software engineering on both Windows and Linux, including Git and an issue tracker.
• You have fine-tuned or quantized open-source models, for example by training LoRAs, and you have run them locally.
• You know the model landscape from the early GPT models to today's open-source ones, including where each one is strong and where it fails.
• You communicate clearly, both within the team and with customers.
• You have a real passion for AI and agents, and you try new tools and ideas without being asked.

Requirements

• It would be great if you have trained object detectors or classifiers for computer vision.
• Own a GPU capable of running local models because you wanted to experiment.
• Have served models with engines such as vLLM or SGLang, or take part in their communities.
• Have a background or a strong interest in data science.
• You do not need to tick every box. If you have the mindset, we will teach you the computer vision and research side.

Benefits

• Agents as your everyday tools.
• You will use Claude, Codex and self-hosted models every day, with no limits on applying them to your work.
• Work that ships.
• Our models already run in production on edge boxes around the world, and your systems decide how fast they improve.
• Real influence.
• We are a small team. You will work directly with all of us and help shape how the whole company works with AI.
• People who think like you.
• We discuss agent behaviour, harness design, and model quirks every day. You will have people to compare notes with.

How to apply

Please submit your resume/CV in English only. Send us links to things you have built, such as repositories, harnesses, write-ups or side projects. Tell us about one problem you hit with agents and how you solved it. We care more about this than about your CV. The interview is one conversation with our technical leads about your own experience with agents and models. We will ask what you have tried, what broke, and what you learned. These are questions that only hands-on experience can answer.