Senior AI Engineer

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.

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Wakapi Web