AI Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Engineer based in Germany. This is a fully remote opportunity for an AI Engineer focused on building production-ready solutions that address real-world challenges at scale. You will combine strong machine learning fundamentals with hands-on engineering to design, train, optimize, and deploy AI systems. The role offers exposure to diverse AI domains, including LLMs, NLP, computer vision, and other emerging technologies. You will work across data preparation, model development, evaluation, deployment, and MLOps. Success in this position requires both technical depth and the ability to operate independently in a fast-changing environment. You will also have the opportunity to translate complex AI concepts into clear insights for non-technical stakeholders. Accountabilities: Design, develop, and deploy machine learning and AI systems for real-world applications at scale. Build, customize, optimize, and maintain AI models for domain-specific use cases. Develop and maintain data mining, preprocessing, feature engineering, and data-labeling pipelines. Work with large datasets to prepare high-quality training and evaluation data. Apply machine learning techniques across areas such as large language models, natural language processing, computer vision, and other AI domains. Train, evaluate, optimize, and continuously improve machine learning models based on performance results. Design and maintain model deployment and MLOps pipelines to support reliable production environments. Deploy and serve models using cloud platforms and containerized environments. Use Docker, Git, and relevant MLOps tools as part of collaborative software development workflows. Analyze model performance, identify limitations, and implement opportunities for improvement. Communicate technical concepts, findings, and project progress clearly to non-technical stakeholders. Requirements: 2–5 years of hands-on experience building and deploying machine learning or AI models in production environments. Strong proficiency in Python. Practical experience with PyTorch and/or TensorFlow. Experience designing and architecting custom AI or machine learning models. Hands-on knowledge of LLMs, NLP, computer vision, or another relevant AI domain. Strong understanding of supervised and unsupervised learning, deep learning architectures, optimization techniques, and model evaluation metrics. Experience with data preprocessing, feature engineering, data mining, and data-labeling pipelines. Familiarity with MLOps and model deployment tools such as MLflow, Kubeflow, or SageMaker. Knowledge of cloud platforms and services used for model training and serving, such as AWS Lambda. Experience with Docker and containerized model deployment. Familiarity with Git and collaborative software development practices. Strong analytical, troubleshooting, and problem-solving abilities.