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Swiggy Delivery Time Prediction – MLOps Pipeline
I build production-ready AI, RAG, and MLOps systems.
IndiaSwiggy Delivery Time Prediction – MLOps Pipeline at Personal Project
Orchestrated a 6-stage DVC pipeline for 45,593 food-delivery orders, engineering features and training a stacked ensemble achieving 3.16 min MAE. Implemented MLflow tracking, automated pytest CI gates, and containerized deployment to AWS EC2 via GitHub Actions.
PythonDVCMLFlowDockerEC2GitHub ActionsCI CDfastAPILightGBMRandom ForestPandasNumPy
Architected an agentic RAG system using LangGraph, FastAPI, React 19, Qdrant, and LLMs, with dynamic query routing and isolated per-session retrieval state. Implemented multi-source ingestion, vector search, SSE streaming, and a DeepEval evaluation harness achieving high relevancy and faithfulness scores.
LangGraphfastAPIReactQdrantLLMRAGVector DatabasesEmbeddingsRerankingDeepEvalPython
Engineered an end-to-end Florence-2 + Phi-2/Phi-3 multimodal AI pipeline for task-driven object detection, integrating MLCoT reasoning with a Structured Object Knowledge Base. Evaluated the system against baseline pipelines for task-aware filtering and assistive computer vision.
PythonPyTorchTransformersMultimodal AIObject DetectionComputer Vision
Pursuing M.Tech in Robotics and Machine Intelligence with a CGPA of 8.67.
Completed Bachelor of Engineering with a CGPA of 7.46.
Pursuing M.Tech in Robotics and Machine Intelligence with a current CGPA of 8.67.
Completed Bachelor of Engineering with a CGPA of 7.46.
Profile sourced from Himalayas.