Senior ML Engineer (LLMs, AWS)

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!, * 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.

Do you have experience in Spark?, * 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., * 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.