Senior ML Engineer (LLMs, AWS)

Provectus
Spain
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Job source

Tech stack

Amazon Web Services Amazon S3 Python (Programming Language) Machine Learning Natural Language Processing Recommender Systems Software Engineering Feature Engineering Large Language Models Multi-Agent Systems Apache Spark Deep Learning
+5 more
Electronic Medical Records AWS Lambda Dask Machine Learning Operations Docker

Job description

  • 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.

Requirements

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.

Apply for this position

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