Responsibilities
- Create ML models from scratch or improve existing models;
- 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;
- Stay updated with the latest developments in ML and AI and propose innovative solutions.
Basic qualifications
- Comfortable with standard ML algorithms and underlying math;
- Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems;
- AWS Bedrock experience strongly preferred;
- Practical experience with solving classification and regression tasks in general, feature engineering;
- Practical experience with ML models in production;
- 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;
- English level - strong Upper- intermediate;
- Excellent communication and problem-solving skills.
- Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda);
- Practical experience with deep learning models;
- Experience with taxonomies or ontologies;
- Practical experience with machine learning pipelines to orchestrate complicated workflows;
- Practical experience with Spark/Dask, Great Expectations.
Benefits
- Long-term B2B collaboration;
- Fully remote setup;
- Comprehensive private medical insurance or budget for your medical needs;
- Paid sick leave, vacation, public holidays;
- Continuous learning support, including unlimited AWS certification sponsorship.
- Recruitment Interview;
- Tech interview;
- HR Interview;
- HM Interview.
Tags & focus areas
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