Karthik R.

Key Skills

Software

AWS SageMaker

CrowdSource

Data Annotation Tech

Google Cloud Vertex AI

Label Studio

Scale AI

Top Subject Matter

Information Technology & Software Engineering

Cloud Computing & DevOps

Artificial Intelligence & Data Analytics

Top Data Types

Text

Document

Computer Code Programming

Top Task Types

Classification

Prompt + Response Writing (SFT)

Transcription

Computer Programming/Coding

Freelancer Overview

I have experience working in software engineering, cloud infrastructure, and AI-assisted engineering environments, where data quality, analysis, and automation play a critical role. Over the past 5+ years as a Site Reliability Engineer and Software Engineer, I have worked extensively with large-scale system monitoring, log analysis, observability platforms, and automation workflows, handling data from thousands of Kubernetes workloads and cloud resources. I have also leveraged AI tools such as GitHub Copilot, Claude AI, and Cursor to improve engineering productivity, streamline debugging, and build intelligent workflows integrated with Jira, Confluence, Grafana, Elasticsearch, and GitHub. What sets me apart is my strong technical background in Python, cloud technologies (AWS, GCP, Azure), Kubernetes, DevOps, and data-driven problem solving. I have designed scalable logging and monitoring platforms, automated operational workflows, built CI/CD pipelines, and implemented reliability engineering practices using metrics, alerts, and analytics. My experience working with large datasets, identifying patterns, ensuring data accuracy, and creating automation solutions makes me well-suited for AI training data, data labeling, evaluation, and quality assurance projects that require attention to detail, analytical thinking, and technical expertise.

Labeling Experience

AI-Assisted Engineering Data Analysis & Quality Evaluation

TextEvaluation/Rating

Worked on AI-assisted engineering initiatives involving the evaluation, validation, and refinement of AI-generated technical content, code suggestions, incident analyses, and operational recommendations. Leveraged AI tools such as GitHub Copilot, Claude AI, and Cursor to support software development, troubleshooting, and infrastructure operations. Responsibilities included reviewing AI-generated outputs for accuracy and relevance, categorizing operational incidents, analyzing large volumes of log and monitoring data, validating recommendations against engineering standards, and providing feedback to improve output quality. Processed data from cloud environments, Kubernetes clusters, monitoring systems, and CI/CD pipelines, ensuring consistency, correctness, and adherence to operational best practices. Applied strict quality control measures through peer reviews, validation against production systems, root cause analysis (RCA), and adherence to reliability engineering principles including SLIs, SLOs, and incident management workflows. Contributed to data-driven decision making by maintaining high standards of accuracy, consistency, and technical correctness

2025 - 2026

Education

Work History