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.
Reportar empleo
