AI Summary
Provectus seeks a Senior ML Engineer with experience in building ML infrastructure to drive end-to-end AI transformations. Key responsibilities include developing experimentation roadmaps, creating ML models from scratch, and collaborating with cross-functional teams.
Key Highlights
Senior ML Engineer
ML Infrastructure expertise
Experimentation roadmaps
Collaboration with engineering, data, and product teams
Key Responsibilities
Create ML models from scratch or improve existing models
Collaborate with engineering, data, and product teams
Develop experimentation roadmaps
Set up a reproducible experimentation environment and maintain experimentation pipelines
Monitor and maintain ML models in production to ensure optimal performance
Technical Skills Required
Benefits & Perks
Fully remote setup
Comprehensive private medical insurance
Paid sick leave
Vacation
Public holidays
Continuous learning support
Job Description
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.
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
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- 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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- 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
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