M

Md Parwez

AI Researcher

Gurugram, Haryana, India0 mo experience
AI EnabledAI ML Practitioner

Key Highlights

  • Achieved 30% increase in answer accuracy for customer interactions.
  • Developed AI-driven products reducing manual load by 50%.
  • Led end-to-end machine learning pipeline for fraud detection.
Stackforce AI infers this person is a Backend-heavy Fullstack Engineer in the Aviation and AI sectors.

Contact

LinkedIn

Skills

Core Skills

Ai/mlLarge Language Models (llm)Machine Learning

Other Skills

.NET Core.NET FrameworkAgile DevelopmentAlgorithmsAmazon Web Services (AWS)Analytical SkillsAngularAngularJSArtificial Intelligence (AI)Azure OpenAIBack-End Web DevelopmentC (Programming Language)C#C++Cascading Style Sheets (CSS)

Experience

0 mo
Total Experience
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Average Tenure
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Current Experience

Indigo (interglobe aviation ltd)

AI/ML Engineer

Aug 2025 – Present · 10 mos · Gurugram · On-site

  • Built and scaled 6eSkai Chatbots, IndiGo’s AI platform supporting millions of monthly customer interactions.
  • Improved answer accuracy by ~30%, task completion by 15–20%.
  • Improved RAG pipelines using Azure Cognitive Search + Document Intelligence (95%+ retrieval accuracy).
  • Enhanced booking, cancellation, fee resolution, and flight status flows with optimized dialogue logic & prompts.
  • Improved platform reliability by fixing core API, routing, and model-related issues.
  • Tech stack: Python, FastAPI, Azure OpenAI, Cognitive Search, LangChain, RAG, Redis, Docker, GitHub.
PythonFastAPIAzure OpenAICognitive SearchLangChainRAG+5

Aionos

Associate Software Engineer - AI & ML

Jul 2025 – Present · 11 mos · Gurugram, Haryana, India · On-site

  • Working on AI and ML Projects

Upgrad

Data Science Intern

Jan 2024 – May 2024 · 4 mos · India · On-site

  • Led end-to-end development of a machine learning pipeline for online fraud detection, enhancing real-time transaction security.
  • Conducted data cleaning, feature engineering, and normalization on large-scale transactional datasets
  • Achieved 98% overall accuracy on both models KNN and Random Forest, with KNN yielding the best fraud recall at 92%, enhancing detection of rare fraudulent events.

Education

Lovely Professional University

Bachelor of Technology — Computer Science and Engineering

Aug 2020 – May 2024

upGrad.com

Data Science — Data Science (ArtificiaI Intelligence & Machine Learning)

Aug 2020 – May 2024

Md Parwez - AI Researcher | Stackforce