Nov 2019 – Present · 6 yrs 5 mos · Hyderabad, Telangana, India · On-site

• LLM - Large Language Models: Fine-Tuning of the LLM’s .Ranking of the responses based on the accuracy, relevance, Concision , truthfulness, Harmfulness of the responses. Describing the reasons for grading. Harmful requests Analysis : Grading of the Harmful requests on how AI has reacted to harmful requests by following the safety guidelines. Successfully met all the quality standards as per the project requirement.
• Audio Transcription : Transcribing the audio by listening - speech to text.
• Accuracy : Judging the Siri response and grading them which depends on decision making and judgemental call as per the response.
• Audio segment annotation: Audio segmentation and Audio Labeling.
• Face Annotation : Face Annotation and Labeling the video.
• Helpfulness : Judging the Siri responses and grading them.
• Entity Tagging : Labeling the different entities based on their nature
• Audio Labeling: Have to identify the different entities in audio and tag them by following guidelines, examples like contacts , music artists, music albums, relationships, locations, point of interests.
• Query Classification : Classifying the Siri queries into the right segment depending on the nature of the request.
• Successfully met the quality standards in all projects worked without any remark in the quality.
• As part of the pilot team, participated in the complete overhaul of guidelines, adhering to the company’s privacy policies.
• The role involves active listening, transcribing audio files, and evaluating Siri’s response and language usage.
• Evaluating responses as per guidelines using gained cultural knowledge and analytical skills to provide detailed insights for Annotation.
• Demonstrated quick learning ability to effectively utilize various Data Annotation Tools.
• Provided enhancement ideas which will help the Annotation process better.
• Successfully met targets and monthly metrics.