Senior Machine Learning Engineer

The team is focused on building high-performance, production-ready AI systems with strong emphasis on model accuracy, reproducibility, optimisation, and scalable deployment architectures.\nWe are looking for a Senior ML / Computer Vision Engineer to lead the design, development, and optimisation of AI systems powering the products. This is a hands-on individual contributor role involving end-to-end ownership of machine learning workflows, including dataset strategy, evaluation protocols, model architecture design, experimentation, and deployment pipelines.\nThe role reports directly to the CTO/CPO, who has a strong ML background and serves as a technical sparring partner while expecting the candidate to drive key ML and Computer Vision decisions independently.\n\nResponsibilities:\n\nOwn and manage end-to-end ML and Computer Vision pipelines from data processing to deployment.\nDesign, develop, and optimize scalable Deep Learning and AI models.\nDefine evaluation protocols, model architectures, and technical strategies.\nBuild reproducible MLOps workflows including experiment tracking and model versioning.\nAnalyze model performance and improve accuracy, scalability, and inference efficiency.\nCollaborate with cross-functional teams to validate and enhance AI solutions.\nMentor junior ML engineers and provide technical guidance.\nWork in an Agile environment with strong ownership of deliverables and timelines.\nIdentify technical risks, raise blockers proactively, and ensure high-quality execution.\nMaintain technical documentation, engineering standards, and development best practices.\n\nNecessary Skills:\n\nMust Have:\n5+ years of production experience in ML, Deep Learning, and Computer Vision.\nStrong expertise in Python, PyTorch, and modern AI/ML workflows.\nHands-on experience with CNN architectures like ResNet, EfficientNet, or similar.\nExperience building and deploying production-grade Computer Vision systems.\nStrong knowledge of data pipelines, preprocessing, augmentation, and validation.\nGood understanding of statistical analysis, model evaluation, and optimization.\nExperience with ordinal classification and multi-output learning techniques.\nSelf-driven experimentation and problem-solving mindset.\nProficiency in NumPy, pandas, scikit-learn, and related Python ML libraries.\nExperience with Git, clean coding practices, and reproducible workflows.\nFamiliarity with AI-assisted development tools and Agile delivery practices.\nAbility to build MVPs quickly and iterate scalable AI solutions efficiently.\n\nGood to Have:\n\nExperience in Medical Imaging, Healthcare AI, and Medical Image Analysis.\nHands-on experience working with small-data ML environments and multi-annotator datasets.\nKnowledge of model optimization and on-device deployment using ONNX, TensorRT, or CoreML.\nFamiliarity with transfer learning, domain adaptation, and uncertainty quantification techniques.\nExperience managing data annotation workflows and annotation tools.\nWorking knowledge of regulated software standards such as IEC 62304 and MDR.