Professional Overview
I am a Computer Science Engineering graduate and an AI Quality Control Specialist with extensive experience in generative AI evaluation, RLHF, and multilingual dataset curation (Telugu, Hindi, English). I specialize in auditing complex data pipelines to enhance foundation models, having actively contributed to high-volume initiatives like Epsilon-Iris (vision-language auditing) and Project Lyra (linguistic quality control). Leveraging my technical background in Python and SQL, I efficiently navigate structured data formats and assess complex prompt responses. I excel at resolving edge cases, applying strict text normalization rules, and mediating annotation conflicts to ensure exact linguistic and factual accuracy. By bridging the gap between raw algorithmic outputs and human-aligned logic, I consistently deliver the high-fidelity training data required to scale robust, production-ready AI agents and machine learning systems.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Evaluated Telugu & Hindi AI outputs for grammatical accuracy and naturalness.
- Conducted rigorous visual QA and multimodal dataset curation.
- Audited generative AI responses as an AI Safety and Policy Evaluator.
- Text normalization rules, resolving edge cases & mediate conflict annotations.
Key Achievements / KPIs
- Achieved high accuracy auditing large-scale multimodal datasets.
- Enhanced Telugu localization quality via strict formatting rules.
- Tracked systemic errors to provide actionable AI model feedback.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Evaluated Telugu & Hindi AI outputs for grammatical accuracy and naturalness.
- Conducted rigorous visual QA and multimodal dataset curation.
- Audited generative AI responses as an AI Safety and Policy Evaluator.
- Text normalization rules, resolving edge cases & mediate conflict annotations.
Key Achievements / KPIs
- Achieved high accuracy auditing large-scale multimodal datasets.
- Enhanced Telugu localization quality via strict formatting rules.
- Tracked systemic errors to provide actionable AI model feedback.
Education
Field of Study: COMPUTER SCIENCE ENGINEERING
Areas of Expertise
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
