Senior AI Engineer
Job Location:
Capital Department - Argentina
Monthly Salary:
Posted on:
Vacancies:
Job Summary
The Role:
The Senior AI Engineer is responsible for designing building and deploying end-to-end machine learning and AI solutions that translate complex business problems into data-driven products. This role requires strong hands-on technical expertise combined with the ability to operate in fast-changing environments and engage directly with stakeholders using data-backed insights.
Responsibilities:
Perform exploratory data analysis (EDA) and data wrangling on raw messy datasets to extract actionable business insights.
Design build train and evaluate end-to-end machine learning models including regression classification clustering and ensemble-based approaches.
Develop and implement predictive analytics solutions using traditional machine learning techniques such as Linear and Logistic Regression Random Forests and XGBoost.
Integrate modern AI capabilities such as NLP generative AI or advanced predictive analytics into existing products and client solutions.
Handle end-to-end ML workflows from data preparation and feature engineering to model validation and deployment support.
Perform basic data engineering and data cleaning tasks independently in environments with evolving infrastructure.
Collaborate closely with clients and internal stakeholders to understand requirements and translate them into technical solutions.
Professionally challenge and defend technical decisions using data metrics and analytical insights when dealing with demanding stakeholders.
Adapt quickly to changing priorities working across multiple initiatives such as model development SQL-based analytics and urgent dashboards.
Contribute to AI solution design in startup-like or consulting environments where ambiguity and rapid iteration are expected.
Requirements:
Bachelors degree in Computer Science Software Engineering or a related field.
A minimum of 5 years of professional experience in data science machine learning or AI engineering roles.
Strong background in data science foundations including exploratory data analysis data wrangling and deriving business insights from complex datasets using Python Pandas and SQL.
Proven experience building training and evaluating traditional machine learning models from scratch including Linear Regression Logistic Regression Random Forests XGBoost classification and clustering techniques.
Demonstrated ability to bridge traditional data science with modern AI applications such as NLP predictive analytics or generative AI.
Hands-on experience delivering end-to-end machine learning solutions in production or client-facing environments.
Comfort working on the dirty work of data including cleaning preprocessing and basic data engineering in the absence of mature infrastructure.
Experience working in consulting freelance or startup environments with evolving roadmaps and limited operational support.
Strong stakeholder management skills with the ability to use data and insights to push back professionally and defend technical decisions.
High adaptability and comfort with frequent context-switching across multiple technical domains.
Ability to operate effectively without a fully mature data warehouse or dedicated MLOps team.
Excellent English communication skills.
C - BD -
About Company
We are passionate about what we do and we strive to be better with each day that goes by.We want to contribute to the generation of a technological community powered by the conviction that, through small actions, we can help create a better world.
