AI Engineer (Computer Vision and Applied GenAI)

Location: Amman, Jordan. Fully remote.

Type: Full-time, permanent.

Level: Mid-level, roughly 3 to 5 years of hands-on experience.

Languages: English required. German is a strong plus.

About the role

We are looking for an AI Engineer based in Amman who will work remotely with a distributed engineering team. This is a builder role, not a research role. You will take AI capabilities from prototype to something that runs reliably in production, and you will own your work end to end: the model, the data around it, the service that serves it, and the evidence that it actually works.

The work spans two areas. On one side, computer vision and perception: camera pipelines, object detection and recognition, and inference that has to run fast and stay stable. On the other, applied generative AI: LLM-based features, retrieval, agents, and the evaluation harnesses that keep them honest. You will not be handed a narrow slice of either. You will be expected to move between them and to build the plumbing that connects them to a real product.

• Design, train, fine-tune and evaluate models for vision tasks (detection, classification, segmentation, tracking) and integrate them into production pipelines.
• Build LLM-powered features: retrieval-augmented generation, tool-using agents, structured extraction, and the prompt and evaluation infrastructure behind them.
• Write the services around the models: APIs, data pipelines, batch and streaming jobs, storage.
• Optimise for the target hardware, including quantisation, batching, and inference on edge devices where cloud inference is not an option.
• Define and track quality metrics. Establish a baseline before claiming an improvement, and be able to show where a number came from.
• Instrument, monitor and debug models in production: drift, latency, failure modes, and the unglamorous work of finding out why a pipeline broke at 3am.
• Work directly with product and business stakeholders to turn a vague need into a scoped, measurable deliverable.
• Document what you build so that the next engineer does not have to reverse-engineer it.

• 3 to 5 years building and shipping machine learning or AI systems in production. Personal projects and Kaggle notebooks alone will not cover this.
• Strong Python. Clean, tested, reviewable code, not notebook-only output.
• Practical depth in at least one of the two areas below, and working familiarity with the other:
• Computer vision: PyTorch or TensorFlow, OpenCV, modern detection and segmentation architectures, dataset creation and annotation workflows.
• Applied GenAI: LLM APIs and open-weight models, RAG, embeddings and vector stores, agent frameworks, prompt design, and systematic evaluation.
• Solid software engineering fundamentals: Git, code review, testing, CI, Docker, and comfort on the Linux command line.
• Experience deploying a model as a service and keeping it running, including cloud deployment (AWS, Azure or GCP) and basic observability.
• SQL and general data handling: you can find, clean and reason about the data before modelling it.
• Fluent written and spoken English, and the self-direction that remote work requires. You are comfortable writing things down, flagging blockers early, and working without someone checking in on you hourly.

Strong plus

• German language skills. Part of the team and a meaningful share of the documentation, meetings and stakeholder communication are in German. Any level from solid B1 upward is a real advantage, and it will widen the scope of what you can own. It is not a hard requirement, and we will support you in improving it.
• Edge and embedded inference: NVIDIA Jetson, TensorRT, ONNX Runtime, OpenVINO.
• Video streaming and industrial camera work: RTSP, GStreamer, GenICam, machine vision cameras.
• MLOps tooling: MLflow, Weights and Biases, DVC, Kubernetes, model registries.
• Experience in an industrial, robotics, IoT or B2B product environment.
• A public track record: open source contributions, technical writing, or published work.

• Competitive salary, benchmarked to the Amman market for this level.
• Fully remote setup.
• Real ownership of features that reach customers, rather than proof-of-concept work that is quietly shelved.
• Direct exposure to the European market and to senior technical decision making.

How we work

• Remote-first, with asynchronous written communication as the default and a reasonable overlap window with the European working day.
• Small teams, short decision paths, and direct access to the people who set priorities.
• We prefer a working pilot with a clear owner and a measurable outcome over a long specification.
• Human oversight, data protection and security are part of the definition of done, not an afterthought bolted on before launch.