As an ML Engineer, you’ll be provided with all opportunities for development and growth.
Let's work together to build a better future for everyone!
Requirements:
- 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
- Experience with data pipelines
- English level - strong 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
- 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
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