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.

C - BD - 28042026

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