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Arabian Agile Professionals

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Adapt your start and end times around your life and workload.

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Work from home for part of your working week.

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Work fully remotely, from home or anywhere agreed.

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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.
<h2>Tasks</h2>
<ul>
<li>Design, train, fine-tune and evaluate models for vision tasks (detection, classification, segmentation, tracking) and integrate them into production pipelines.</li>
<li>Build LLM-powered features: retrieval-augmented generation, tool-using agents, structured extraction, and the prompt and evaluation infrastructure behind them.</li>
<li>Write the services around the models: APIs, data pipelines, batch and streaming jobs, storage.</li>
<li>Optimise for the target hardware, including quantisation, batching, and inference on edge devices where cloud inference is not an option.</li>
<li>Define and track quality metrics. Establish a baseline before claiming an improvement, and be able to show where a number came from.</li>
<li>Instrument, monitor and debug models in production: drift, latency, failure modes, and the unglamorous work of finding out why a pipeline broke at 3am.</li>
<li>Work directly with product and business stakeholders to turn a vague need into a scoped, measurable deliverable.</li>
<li>Document what you build so that the next engineer does not have to reverse-engineer it.</li>
</ul>
<h2>Requirements</h2>
<ul>
<li>3 to 5 years building and shipping machine learning or AI systems in production. Personal projects and Kaggle notebooks alone will not cover this.</li>
<li>Strong Python. Clean, tested, reviewable code, not notebook-only output.</li>
<li>Practical depth in at least one of the two areas below, and working familiarity with the other:</li>
<li>Computer vision: PyTorch or TensorFlow, OpenCV, modern detection and segmentation architectures, dataset creation and annotation workflows.</li>
<li>Applied GenAI: LLM APIs and open-weight models, RAG, embeddings and vector stores, agent frameworks, prompt design, and systematic evaluation.</li>
<li>Solid software engineering fundamentals: Git, code review, testing, CI, Docker, and comfort on the Linux command line.</li>
<li>Experience deploying a model as a service and keeping it running, including cloud deployment (AWS, Azure or GCP) and basic observability.</li>
<li>SQL and general data handling: you can find, clean and reason about the data before modelling it.</li>
<li>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.</li>
</ul>
Strong plus
<ul>
<li>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.</li>
<li>Edge and embedded inference: NVIDIA Jetson, TensorRT, ONNX Runtime, OpenVINO.</li>
<li>Video streaming and industrial camera work: RTSP, GStreamer, GenICam, machine vision cameras.</li>
<li>MLOps tooling: MLflow, Weights and Biases, DVC, Kubernetes, model registries.</li>
<li>Experience in an industrial, robotics, IoT or B2B product environment.</li>
<li>A public track record: open source contributions, technical writing, or published work.</li>
</ul>
<h2>Benefits</h2>
<ul>
<li>Competitive salary, benchmarked to the Amman market for this level.</li>
<li>Fully remote setup.</li>
<li>Real ownership of features that reach customers, rather than proof-of-concept work that is quietly shelved.</li>
<li>Direct exposure to the European market and to senior technical decision making.</li>
</ul>
How we work
<ul>
<li>Remote-first, with asynchronous written communication as the default and a reasonable overlap window with the European working day.</li>
<li>Small teams, short decision paths, and direct access to the people who set priorities.</li>
<li>We prefer a working pilot with a clear owner and a measurable outcome over a long specification.</li>
<li>Human oversight, data protection and security are part of the definition of done, not an afterthought bolted on before launch.</li>
</ul>

AI Engineer (Computer Vision and Applied GenAI)

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