Job Description

Job Description
The position is aimed at the development and deployment of advanced artificial intelligence and machine learning solutions, participating in the entire model life cycle, from data analysis to automation and maintenance in production.
Responsibilities:
Model development:
design, train, tune and evaluate models covering classical machine learning, learning such as CNN and transformers, and generative artificial intelligence such as LLM and diffusion models.
Data exploration and analysis:
Perform exploratory data analysis, statistical testing, and time series analysis and forecasting to inform business functions, directions, and KPIs.
LLM and Agentic AI Engineering:
create prompts, tune large language models with domain-specific data sets to increase accuracy, and align results with product requirements.
Automation and integration of artificial intelligence:
expose AI components as microservices and event-driven workflows, integrate with orchestration tools like Airflow or Prefect and with business APIs to automate.
Continuous learning:
track progress in language models, computer vision and analytics, share knowledge and practices with the broader engineering team.
Mentor junior engineers and contribute to the definition of technical direction and best engineering practices.
Requirements:
Bachelor's degree in Computer Science, Mathematics or a related field.
More than five years of professional experience working on artificial intelligence and machine learning projects.
Good command of English, both written and spoken.
Proficiency in Python and core libraries such as PyTorch or TensorFlow, scikit-learn, pandas and NumPy.
Understanding machine learning algorithms, learning fundamentals, and basic statistics.
Experience in data manipulation and visualization with tools such as Matplotlib or Plotly, as well as exploratory analysis
Familiarity with at least one of the following technologies: OpenCV, Hugging Face Transformers, LangChain, MLflow or similar.
Knowledge of software engineering practices, including Git, code reviews, testing, and integration processes.

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