AI Engineer – Computer Vision, Applied GenAI

• Design, train, fine-tune, and evaluate models for detection, classification, segmentation, and tracking
• Integrate computer vision models into production pipelines
• Build LLM-powered features including RAG, tool-using agents, and structured extraction
• Build prompt and evaluation infrastructure
• Write APIs, data pipelines, batch jobs, streaming jobs, and storage services around models
• Optimize models for target hardware through quantization, batching, and edge-device inference
• Define and track quality metrics and establish measurable baselines
• Instrument, monitor, and debug production models for drift, latency, and failure modes
• Work with product and business stakeholders to create scoped, measurable deliverables
• Document systems and features for future engineers
• Own AI work end to end from model and data through production service and validation evidence

• 3 to 5 years building and shipping machine learning or AI systems in production
• Strong Python
• Clean, tested, reviewable code; not notebook-only output
• Practical depth in computer vision or applied generative AI, with working familiarity in the other area
• 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 Linux command line
• Experience deploying a model as a service and maintaining it in operation
• Cloud deployment experience with AWS, Azure, or GCP
• Basic observability
• SQL and general data handling
• Fluent written and spoken English
• Self-direction required for remote work
• German is a strong plus; B1 or higher is advantageous but not required
• Edge and embedded inference experience with NVIDIA Jetson, TensorRT, ONNX Runtime, or OpenVINO
• Video streaming and industrial camera experience with RTSP, GStreamer, GenICam, or machine vision cameras
• MLOps tooling experience with MLflow, Weights and Biases, DVC, Kubernetes, or model registries
• Experience in an industrial, robotics, IoT, or B2B product environment
• Public track record through 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
• Direct exposure to the European market and to senior technical decision making
• Asynchronous written communication as the default
• Reasonable overlap window with the European working day
• Small teams, short decision paths, and direct access to the people who set priorities
• Human oversight, data protection and security integrated into the definition of done