Provectus
AI and ML technologies, cloud services, and data engineering
SeniorMLEngineer(LLMs,AWS)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior ML Engineer (LLMs, AWS) at Provectus. Skills: LLMs, AWS, Python, ML models. Create ML models. Improve existing models”
What You'll Achieve.
ensure optimal performance
Industry & Context.
Excellent problem-solving skills
What They're Looking For.
Must Have
standard ML algorithms, LLMs in production, RAG architecture, agentic systems, classification and regression tasks, feature engineering, ML models in production, NLP, LLMs, Recommendation engines, Solid software engineering skills, Python expertise, Docker, English level - upper- intermediate
Nice to Have
AWS stack, Amazon SageMaker, ECR, EMR, S3, AWS Lambda, deep learning models, taxonomies or ontologies, machine learning pipelines, Spark/Dask, Great Expectations, AWS Bedrock experience
What You'll Do.
Improve existing models
Develop experimentation roadmap
Set up reproducible experimentation environment
Maintain experimentation pipelines
Monitor ML models in production
Maintain ML models in production
How You'll Work.
Team & Collaboration
Collaborate with engineering team; Collaborate with data scientists; Collaborate with product managers
Communication Scope
Excellent communication skills
Full Job Description
## Description Join us at Provectus to be a part of a team that is dedicated to building cutting-edge 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 what's possible. 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. English level - strong upper- intermediate. Excellent communication and problem-solving 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 Spark/Dask, 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 m
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