---
title: "Vikranth M D - Machine Learning Engineer in East Godavari | Tal"
description: "Meet Vikranth M D, CSE student with a keen interest in artificial intelligence and machine learning based in East Godavari. AI stack includes claude code, gemini, and hugging face. Explore public work links including github and medium."
image: "https://tal.club/meet/vikranth-magharla-dhanaraju/share-image"
---

# Vikranth M D on Tal

## Headline

CSE student with a keen interest in artificial intelligence and machine learning

## Location

East Godavari

## AI stack

claude code

gemini

hugging face

langgraph

openai

LlamaIndex

## Work experience

Machine Learning Engineer at Quantiphi Analytics Solutions, Feb 2024 \- Present · 2 yrs 8 mos, On\-site, Intelligent Document Processing Pipeline for Insurance \| LangChain, RAG, ChromaDB, AWS, GitLab CI/CD • Automated manual document workflows for an entire processing department, accelerating end\-to\-end processing speed by 90% for 8,000\+ documents/month while maintaining a multi\-cloud \(AWS \+ GCP\) GenAI system across 4 environments via GitLab CI/CD\. • Designed cascaded LLM routing \(Nova\-Lite to Claude 3 Sonnet\) with regex\-based ACORD detection; implemented per\-token cost tracking across model providers and CloudWatch dashboards to alert on model degradation\. • Built a RAG validation subsystem \(ChromaDB \+ Sentence Transformers\) that flagged out\-of\-distribution LLM outputs; reduced hallucination rate from 12% to 3% and improved extraction accuracy from 78% to 92%, tracked as live SLA metrics\. • Engineered a post\-processing validation engine with business\-rule enforcement and RapidFuzz\-based OCR correction, reducing manual post\-editing by 42% and enabling automated downstream ingestion into visualization and downstream systems\. • Reduced Kubernetes cold\-start latency by 65% \(avg 12s → 4\.2s\) via PyInstaller single\-binary Docker builds; implemented structured logging and centralized exception handling, improving throughput by 18%\. Automated Gemini Fine\-Tuning Pipeline \| GCP, Vertex AI, Cloud Run, Cloud Functions, Python, MLOps • Cut Vertex AI compute utilization by 35–40% by implementing distributed locking, dataset validation, and delta\-based retraining thresholds, eliminating redundant training runs\. • Built an event\-driven LLM fine\-tuning pipeline on GCP \(Vertex AI, Cloud Run Jobs\) with automated job status monitoring and self\-healing recovery mechanisms for continuous model reliability\. • Reduced mean time to recovery \(MTTR\) for retraining incidents by automating the fine\-tuning lifecycle – orchestration, real\-time monitoring, metadata tracking, and alerting dashboards – enabling proactive failure detection\. • Accelerated production release cycles by documenting

Machine Learning Engineer at Quantiphi Analytics Solutions, Feb 2024 \- Aug 2024 · 7 mos, Hybrid

Data Science Intern at PRs Software Services, Aug 2023 \- Nov 2023 · 4 mos, India

Intern at Nippon Paint India, Aug 2022 \- Sep 2022 · 2 mos, India, Conducted market research on the Decorative\-paints industry Developed marketing strategies for new product launches Analyzed customer data to identify trends and opportunities

Social Media Marketing Team Lead at Growth Heads Digital, Dec 2020 \- Dec 2021 · 1 yr 1 mo

## Education

Bachelor of Technology \- BTech, Computer Science, Siksha O Anusandhan Corporate Relation

Bachelor of Technology, Computer Engineering, Siksha 'O'​ Anusandhan University

## Public links

- github: https://github\.com/vickymdn
- medium: https://medium\.com/@mdvikranth

[View the original profile](https://tal.club/meet/vikranth-magharla-dhanaraju)

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