Data Scientist / ML Engineer (AWS, GenAI & MLOps) / Hybrid Madrid / (H/X/M) en España

• University degree in Computer Engineering, Telecommunications, Mathematics, Statistics, or other STEM disciplines.
• Minimum of 3 years of experience in Data Scientist, Machine Learning, or related positions.
• Demonstrable experience in:
• • Python
  • Data pipeline development
  • Machine Learning and Artificial Intelligence in production
  • Integration of solutions via APIs
• Experience working with AWS services such as:
• • SageMaker
  • Bedrock
  • S3
  • IAM
• Experience in NLP projects and the use of LLMs.
• Knowledge of Prompt Engineering applied to transcription analysis and natural language-based process automation.
• Experience in Snowflake:
• • Data modeling
  • Table creation
  • Environment management
• Advanced knowledge of SQL.
• Experience deploying solutions in production environments.

• Experience in multinational environments or multi-country architectures.
• Experience in AWS multi-account environments.
• Knowledge and experience in MLOps practices.
• Experience in AIOps or AI-based operations automation.
• Additional experience in Generative AI and advanced cloud ecosystems.
• Participation in Machine Learning model industrialization projects.

At Experis, we are looking to hire a Data Scientist / ML Engineer to participate in innovative projects related to Artificial Intelligence, Machine Learning, and advanced data processing, collaborating on the design, development, and implementation of AI solutions in production environments on AWS.

The main responsibilities will be:

• Design, develop, and maintain business-oriented Machine Learning and Artificial Intelligence models.
• Build and optimize data pipelines for model training, deployment, and monitoring.
• Implement AI solutions in production environments using AWS cloud services.
• Participate in Natural Language Processing (NLP) projects and transcription analysis using LLMs.
• Design and optimize prompt engineering strategies for generative artificial intelligence-based use cases.
• Integrate solutions via APIs with internal systems and external platforms.
• Develop data ingestion, transformation, and exploitation processes on Snowflake.
• Collaborate with multidisciplinary teams to ensure the scalability, traceability, and maintenance of deployed solutions.
• Apply MLOps best practices for the industrialization of AI models.

• Location: Madrid.
• Hybrid model with on-site presence approximately 2 days per week at the client's site.
• Split working day from Monday to Thursday.
• Half-day Fridays.
• Intensive schedule during the summer period.