Key Responsibilities

• Design, build, and optimize data and ML pipelines for training and inference.
• Develop and deploy scalable, production-ready machine learning models and systems.
• Manage infrastructure for model serving, monitoring, and performance optimization.
• Clean, validate, and preprocess data to ensure high quality and integrity.
• Translate data science prototypes into robust production code and services.
• Research, select, and implement appropriate ML algorithms and tools for business needs.
• Train, retrain, fine-tune, and evaluate models to maintain accuracy and reliability.
• Perform statistical analysis and testing to improve model performance.
• Collaborate closely with Data Engineers, Data Scientists, and Software Engineers to integrate ML solutions.
• Document ML system design, development processes, and operational workflows.

Requirements

• Bachelor's degree in Computer Science, Data Science, or a related field, with 2-3 years of hands-on experience in machine learning, data science, or applied Al.
• Strong programming skills in Python and experience working with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Experience deploying machine learning models into production, including APIs, dashboards, or integrated systems, with familiarity in MLOps tools and workflows.
• Working knowledge of SQL, database design, ETL pipelines, Docker, Git, and data visualization tools.
• Exposure to financial inclusion or small enterprise data, with an interest in ethical, explainable Al and social impact analytics.