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Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses.
Job Responsibility:
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, GCP, ECS, EMR/Glue, S3, Lambda, SQS)
English level - Upper Intermediate
Excellent communication and problem-solving skills
Nice to have:
Experience with RAG
Experience with taxonomies or ontologies
Practical experience with Spark/Dask, Great Expectations
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