About Skills Work Experience Contact
GitHub LinkedIn
Resume PDF
AI Engineer  |  Agentic AI, GenAI & LLM Workflows

AI Engineer Agentic AI, GenAI & LLM Workflows

I build autonomous, tool-using AI systems — retrieval-grounded assistants and multi-agent pipelines that plan, act, and self-correct.

meta_ai_agent_runtime.py
LIVE AGENT RUNTIME
Ready • Click any chip to run live simulation
SCROLL
(01) About

I design and build AI systems that solve complex problems and create real impact.

I'm an AI Engineer specializing in Agentic AI, Generative AI and LLM workflows. I enjoy building end-to-end systems — from data pipelines and model training to orchestration, evaluation and deployment. My focus is creating scalable, reliable and impactful AI solutions, backed by a strong foundation in machine learning and big-data technologies.

0.00
CGPA — B.Tech AI & DS
IRJET
Published (IF 8.315)
0
Industry Internship
0+
Projects Built
(02) Capabilities

A full-stack AI toolkit, from agents to infra.

01 / Orchestration
LangChain GPT-4o Groq

Agentic AI & LLM Workflows

LangChain agents, LLM-assisted auditing, counterfactual reasoning and natural-language Q&A over ML experiments.

02 / AutoML Optuna • SHAP

Automated ML

End-to-end AutoML: preprocessing, feature engineering, tuning and interpretability.

03 / Generation Prompting • Gradio

Generative AI

Prompt engineering, structured output and abstract generation from LLM backends.

04 / Core

Python & Data

Clean, production Python and high-performance data microservices.

05 / Big Data

Data Engineering

SQL analysis, EDA and large-scale processing with Apache Spark and Hadoop.

06 / Delivery

MLOps & Deploy

Containerized, reproducible model delivery with FastAPI, Docker & MLflow.

(03) Selected Work

Systems that think, act, and ship.

01 2026 • Published IRJET (IF 8.315)

MetaAI — Automated ML Pipeline Platform

A production-grade AutoML platform with a unified 8-module pipeline — from CSV ingestion and MICE imputation to Optuna tuning, SHAP explainability, fairness auditing and one-click FastAPI export. Adds LLM agentic auditing for post-mortems and NL Q&A. Published in IRJET (IF 8.315).

LangChain Optuna SHAP FastAPI Gradio Docker
0.00% Accuracy
0.000 AUC-ROC
0ms Latency
MetaAI Platform
02 2024 • MovieLens Benchmark

Recommender System — MovieLens

Built and evaluated recommender systems on the MovieLens dataset (100K ratings, 9K movies) — popularity, user-user collaborative filtering and matrix factorization — via a modular pipeline. Matrix factorization gave the best RMSE, Recall@K and NDCG@K.

Pandas NumPy SciPy Scikit-learn
0K Ratings
SVD / MF Best model
MovieLens Recommender
03 2024 • Predictive Modeling

Flight Cancellation Prediction

Predicted flight cancellations end-to-end: cleaning, outlier removal and encoding, EDA to surface weather/delay/time trends, then Logistic Regression, Decision Tree, Random Forest and SVM classifiers benchmarked head to head.

Scikit-learn Pandas EDA
4 Models Tested
Random Forest Top F1
Flight Prediction
(04) Trajectory
May 2024 — Jun 2024

Data Analyst Intern

@ The Apollo Institute of Medical Science & Research

Extracted and analyzed datasets using SQL to generate key healthcare and operational insights, presented data-driven reports to leadership, and trained team members on data analysis best practices.

2022 — 2026 • CGPA 8.67

B.Tech — CSE (AI & Data Science)

@ The Apollo University, Chittoor

Coursework and projects spanning machine learning, big-data technologies (Spark, Hadoop), agentic AI systems, and automated machine learning pipelines.

Email copied to clipboard!