Phani Kishore Jarugumilli

Associate Consultant

Vishakhapatnam, Andhra Pradesh, India2 yrs 5 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in evaluating AI-generated content quality.
Strong background in large language models and AI.
Proven track record in data annotation and prompt engineering.

Stackforce AI infers this person is a skilled AI Analyst specializing in generative AI and machine learning quality assurance.

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Skills

Core Skills

Artificial Intelligence (ai)Large Language Models (llm)

Other Skills

Data AnnotationPrompt EngineeringAttention to DetailRelevanceGen AiSQLResponsive WebsiteCascading Style Sheets (CSS)Python (Programming Language)Full-Stack DevelopmentHTMLStatic WebsiteBootstrap

About

Working on various writing types and ranking the responses produced by generative AI model by assessing the information provided by AI model is factually right or not by performing a quality verification.

Experience

2 yrs 5 mos

Total Experience

2 yrs

Average Tenure

5 mos

Current Experience

Innodata inc.

Analyst AI/LLM Practice

Nov 2025 – Present · 5 mos · Noida, Uttar Pradesh, India · Remote

● Evaluated and annotated LLM generated outputs to improve instruction adherence, response
accuracy, and contextual relevance.
● Performed prompt -response quality analysis, reasoning gaps, and policy violations.
● Applied detailed prompting guidelines to ensure safe, unbiased, and high quality AI
outputs.
● Supported cross functional AI teams by aligning data outputs with model objectives and
evaluation criteria.
● Provided structured feedback to improve prompt templates and annotation standards.

Data AnnotationPrompt EngineeringArtificial Intelligence (AI)Large Language Models (LLM)

Amazon

ML Data Associate

Sep 2023 – Sep 2025 · 2 yrs · Hyderabad, Telangana, India · On-site

● Supported generative AI and machine learning models by reviewing, ranking, and validating
large-scale text and video outputs.
● Analyzed LLM responses for instruction following accuracy, factual consistency, safety, and tone
alignment.
● Identified edge cases, ambiguous prompts, and failure patterns affecting model
performance.
● Maintaining high accuracy and productivity metrics in a high-volume AI training
environment.
● Contributed insights that improved prompt clarity, reduced response ambiguity, and
strengthened model reliability.