**Responsibilities**: - Create ML models from scratch or improve existing models. - Create ML/AI pipelines that include custom models or APIs as part of the processing. - Collaborate with the engineering team, data scientists, and product managers on production models. - Develop experimentation roadmap. - Set up a reproducible experimentation environment and maintain experimentation pipelines. - Monitor and maintain ML models in production to ensure optimal performance. - Write clear and comprehensive documentation for ML models, processes, and pipelines. **Requirements**: - Comfortable with standard ML algorithms and underlying math. - Practical experience with solving classification and regression tasks in general, feature engineering. - Practical experience with ML models in production: orchestrating workflows, monitoring metrics. - Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. - Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts). - Python expertise, Docker. - Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECS, EMR/Glue, S3, Lambda, SQS). - English level - Upper Intermediate. - Excellent communication and problem-solving skills. **Will be a plus**: - Experience with RAG. - Experience with taxonomies or ontologies. - Practical experience with Spark/Dask, Great Expectations.