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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

- Bachelor’s 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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