AI Engineer (Computer Vision and Applied GenAI)

• 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.

• 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.

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. Tasks • 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. Requirements • 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. Benefits • 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. Originally posted on Himalayas AI-Engineer, Machine-Learning-Engineer, Computer-Vision-Engineer, Applied-AI-Engineer, Computer-Vision-ML-Engineer, Computer-Vision-and-Machine-Learning-Engineer, Artificial-Intelligence-Engineer, AI-Development-Engineer