Bias in natural language processing systems can perpetuate and exacerbate societal inequalities, reflecting and potentially amplifying existing biases in human language and culture. Amharic, as the official language of Ethiopia, holds cultural and linguistic significance, making it imperative to assess potential biases within its computational representations. This research paper investigates the presence and extent of gender bias in Amharic text corpora. The research utilizes gendered word pairs to capture gender representation in the word embeddings and quantifies the degrees of gender bias present in profession words. We found that profession words carried stereotypical implicit biases with most occupations leaning towards male. Profession words like “nurse” and “house-maid” align with societal gender dynamics, displaying significant female associations. Additionally, professions in the arts and athleticism demonstrate a robust female-leaning bias, while physically demanding and educated professional roles tend to exhibit male-leaning biases. The study contributes insights into the gender dynamics encoded within the Amharic language informing strategies to reduce bias and fostering fair and unbiased representations for improved societal and technological outcomes.