Vikas Bollepalli

Senior Analyst with approximately two years of experience across AI data operations, quality review and content moderation, spanning eight Google AI projects. Promoted to Senior Analyst within fourteen months after ranking among the top 50 performers out of roughly 200 analysts, and later selected as one of only 20 Senior Analysts asked to present project workflows directly to Google product managers. Maintained 90–95% quality while reviewing 300+ tasks daily, and mentored 15 analysts through their transition into live production.

Currently expanding expertise in AI-driven product development through an independent consumer technology initiative.

Founder & Product Lead at Trinetra (2025-08 – Present)

Independent Consumer-Awareness Initiative

• Own end-to-end product direction for an independent consumer-awareness initiative that helps people understand packaged products and make more informed purchasing decisions.
• Research ingredient information, nutrition data and common consumer concerns, then translate the findings into simplified, easy-to-understand product insights.
• Use AI-assisted research methods to speed up market analysis and validate product ideas before committing time to building them out.
• Shape the overall user experience from initial concept through ongoing refinement, prioritising clarity and everyday usability over technical complexity.
• Manage every stage of the initiative independently, including research, feature planning, testing and continuous improvement, while committing more than 40 hours a week to the work.

Senior Analyst at Teleperformance Global Services Pvt. Ltd. (2024-12 – 2025-07)

Promoted from Analyst within fourteen months

• Managed an average workload exceeding 300 operational reviews daily, increasing to 610 during peak production, while consistently maintaining 90–95% quality.
• Sustained productivity of up to 140% against target while continuing to meet client SLAs in a fast-paced, deadline-driven operational environment.
• Earned promotion to Senior Analyst after being selected among the top 50 performers from approximately 200 analysts, based on quality, productivity and overall operational performance.
• Coached and reviewed the production work of approximately 15 analysts before they moved to independent work, reinforcing quality standards and consistent guideline interpretation.
• Took on a temporary quality assurance role during mentoring cycles, checking work before submission and catching errors ahead of client review.
• Prepared daily and weekly operational reports covering productivity, quality, SLA adherence and error trends for management review.
• Collaborated with Team Leads, Quality Analysts and Managers to resolve complex guideline interpretations, maintaining consistent decision-making across Google AI projects.
• Received client and internal recognition for consistently high-quality work, including performance incentives and Amazon gift vouchers during periods of error-free delivery.
• Selected as one of only 20 Senior Analysts from roughly 600 analysts and senior analysts to walk Google Product Managers through project workflow and operational process for the Hydroneer project during a client visit.

Analyst at Teleperformance Global Services Pvt. Ltd. (2023-10 – 2024-11)

• Contributed to eight Google AI projects spanning image and video evaluation, reverse prompting, gameplay annotation, bounding box annotation, content moderation, AI response evaluation, movie summary assessment and music classification.
• Reviewed 100–150 AI-generated images and videos daily, identifying visual defects such as missing objects, distortion and artefacts, and providing 200–400 structured feedback comments to improve output quality.
• Wrote roughly 50 reverse prompts daily by working backward from AI-generated images to reconstruct the likely original prompt, supporting model training and evaluation.
• Annotated gameplay sessions across Hydroneer, Satisfactory, Goat Simulator and Valheim, logging 150–300 behavioural annotations per session to support AI agent training.
• Labelled an average of 250 images daily using bounding box techniques, rising to 450 at peak, accurately marking 5–10 objects per image for object detection model training.
• Moderated 50–150 pieces of user-generated content daily across text, image and video formats, applying policy guidelines, identifying violations and escalating complex cases.
• Assessed movie summaries for accuracy, spoiler sensitivity and reader engagement across 20–50 titles daily, selecting the strongest versions to support recommendation quality.
• Classified 10–40 music clips daily by genre and instrumentation, improving how the recommendation system interpreted audio content.
• Validated AI-generated answers across five independent conversations per question for JEE, NEET and EAMCET examination topics, comparing responses against scholar-approved solutions alongside university professors as part of Google AI Studio academic evaluation.
• Maintained performance in the top 10% of the team while adapting quickly to new projects and evolving guidelines across all eight workstreams.

B.Tech in Computer Science Engineering – TKR College of Engineering & Technology (2017 – 2021)

Intermediate in MPC – KLN Junior College (2015 – 2017)