AI Engineer with 6+ years of experience building, evaluating, and improving production-grade AI and LLM systems, with deep hands-on expertise in AI output evaluation, data annotation, model quality assessment, and content moderation at scale. Proven ability to annotate and label complex datasets across text, code, and structured formats according to detailed project guidelines, evaluate and rate AI-generated responses for quality, accuracy, relevance, and safety, and provide consistent, reliable output that directly improves model training pipelines and evaluation benchmarks. Strong written English, exceptional attention to detail, and a demonstrated ability to follow complex annotation instructions with high consistency across extended projects. Self-motivated, reliable, and experienced working independently in fully remote environments with flexible availability to accommodate project timelines and deliverable deadlines. Passionate about advancing AI systems through rigorous, high-quality evaluation and annotation work that translates directly into measurable improvements in model reliability and output quality.

Avinash Thatavarthi

AI Engineer with 6+ years of experience building, evaluating, and improving production-grade AI and LLM systems, with deep hands-on expertise in AI output evaluation, data annotation, model quality assessment, and content moderation at scale. Proven ability to annotate and label complex datasets across text, code, and structured formats according to detailed project guidelines, evaluate and rate AI-generated responses for quality, accuracy, relevance, and safety, and provide consistent, reliable output that directly improves model training pipelines and evaluation benchmarks. Strong written English, exceptional attention to detail, and a demonstrated ability to follow complex annotation instructions with high consistency across extended projects. Self-motivated, reliable, and experienced working independently in fully remote environments with flexible availability to accommodate project timelines and deliverable deadlines. Passionate about advancing AI systems through rigorous, high-quality evaluation and annotation work that translates directly into measurable improvements in model reliability and output quality.

Available to hire

AI Engineer with 6+ years of experience building, evaluating, and improving production-grade AI and LLM systems, with deep hands-on expertise in AI output evaluation, data annotation, model quality assessment, and content moderation at scale.

Proven ability to annotate and label complex datasets across text, code, and structured formats according to detailed project guidelines, evaluate and rate AI-generated responses for quality, accuracy, relevance, and safety, and provide consistent, reliable output that directly improves model training pipelines and evaluation benchmarks. Strong written English, exceptional attention to detail, and a demonstrated ability to follow complex annotation instructions with high consistency across extended projects. Self-motivated, reliable, and experienced working independently in fully remote environments with flexible availability to accommodate project timelines and deliverable deadlines. Passionate about advancing AI systems through rigorous, high-quality evaluation and annotation work that translates directly into measurable improvements in model reliability and output quality.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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Language

English
Fluent

Work Experience

AI Engineer at JPMorgan Chase
June 1, 2024 - Present
Annotated and labeled complex datasets across text, code, and structured data formats according to detailed project guidelines; reviewed 500+ annotation examples per sprint; maintained annotation consistency rates above 96% across multiple data streams contributing to LLM training and evaluation data quality. Evaluated and rated AI-generated responses for quality, accuracy, relevance, and safety by designing and operating custom eval harnesses and LLM-as-judge scoring pipelines that covered 500+ domain-specific test scenarios; provided structured quality ratings and actionable feedback to inform model training, prompt engineering, and guardrail improvements. Moderated AI-generated content to ensure policy compliance by applying content safety policies, identifying harmful or biased outputs, and flagging edge cases with detailed feedback for remediation teams, improving content safety guardrails over time. Delivered consistent annotation and evaluation output within deadlines by managin
AI & Machine Learning Engineer at Cognizant Technology Solutions
February 1, 2021 - December 1, 2022
Annotated and labeled complex training datasets across text, structured data, and code formats for enterprise AI model development; maintained annotation consistency rates above 94% across batches and contributed to AI model quality improvements. Evaluated AI-generated responses via Python-based evaluation pipelines with LLM-as-judge scoring and human review queues, driving 18% improvement in model accuracy. Moderated AI-generated content for policy compliance and safety, producing detailed moderation reports with specific policy violation citations and remediation actions. Independently managed annotation task queues across multiple concurrent projects, delivering all batches on time with quality above team averages. Built Python automation scripts to support large-scale annotation consistency analysis, improving throughput by 40%. Proactively communicated project status and blockers in a fully remote setup.
Software & AI Engineer at JK Fenner
July 1, 2018 - December 1, 2020
Performed data annotation and labeling for AI model training datasets across text, structured data, and code formats, ensuring high consistency and meeting quality standards for downstream model training. Evaluated AI-generated outputs, identifying errors, inconsistencies, policy violations, and providing structured ratings and explanations. Reviewed and moderated AI-generated content for policy compliance and safety, escalating recurring issue patterns to refine guidelines. Built Python scripts to automate repetitive data preprocessing and annotation consistency checks; delivered annotations on schedule. Communicated feedback and issues clearly through structured written reports and adapted to evolving guidelines while working remotely.

Education

Master of Science in Computer Science (Data Science Emphasis) at University of Alabama at Birmingham (UAB)
January 1, 2023 - April 1, 2024

Qualifications

AWS Certified Developer – Associate
January 11, 2030 - April 9, 2026
Microsoft Certified: Azure Developer Associate (AZ-204)
January 11, 2030 - April 9, 2026
Microsoft Certified: .NET Application Development
January 11, 2030 - April 9, 2026

Industry Experience

Financial Services, Professional Services, Software & Internet

Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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