Offer summary

Qualifications:

Strong hands-on experience with large language models (LLMs) in production., Proficiency in Python and Docker for software development., Experience with ML algorithms, feature engineering, and solving classification/regression tasks., Good communication skills in English and a solid understanding of cloud platforms like AWS..

Key responsibilities:

Develop and improve ML models, including from scratch.

Collaborate with cross-functional teams on production models.

Maintain and monitor ML models in production to ensure performance.

Document ML processes, models, and pipelines clearly.

Job description

Join us at Provectus to be a part of a team that is dedicated to building cuttingedge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of whats possible.

As an ML Engineer, you’ll be provided with all opportunities for development and growth.

Lets work together to build a better future for everyone!
Requirements:Comfortable with standard ML algorithms and underlying math.
Strong handson 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 wellstructured modules, not only notebook scripts).
Python expertise, Docker.
English level strong Intermediate.
Excellent communication and problemsolving skills.

Will be a plus:
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 SparkDask, Great Expectations.

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.