Machine Learning Engineer

Role overview Develop and integrate machine learning models that improve the accuracy, performance, and adaptability of AI-driven workflows. You’ll partner with AI developers, automation engineers, and solution architects to bring models into operational environments and use production feedback to guide improvements.

Responsibilities - Develop, train, and deploy custom or fine-tuned models for agent systems and automated workflows. - Integrate language and specialized models into agent infrastructure, considering accuracy, performance, and compliance. - Build data pipelines for training, inference, and learning from interactions, logs, and business system data. - Implement embeddings, vector databases, and memory approaches to support retrieval and context. - Turn business rules into decision models, classifications, or recommendations in collaboration with agent developers. - Monitor production results and manage model tuning, retraining, or replacement; apply MLOps practices to lifecycle pipelines.

Requirements - At least three years of experience building and deploying machine learning models. - Ability to integrate models into real-time systems and work with business data, including structured and unstructured inputs. - Knowledge of model performance monitoring and production deployment practices. - Ability to collaborate with engineering and architecture teams on model integration and compute needs.

Nice to have - Familiarity with MLOps tools such as MLflow, Kubeflow, ClearML, or model registries. - Knowledge of data governance, privacy, and AI compliance requirements.

Benefits and work setup - Full-time remote position with flexible time and location, subject to working from an eligible country. - Annual training allowance of 12 days, including six days chosen by the employee. - Team activities and a salary package are mentioned; specific compensation details are not provided.