AG
Available for opportunities

Hello, I'm A b h i l a s h G a n j i

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I architect production ML systems that scale. From Large Language Models to Recommender Engines, I transform complex data into intelligent systems that drive real business impact. 7+ years of building AI that works at scale.

0 + Years Experience
0 + ML Models Deployed
0 Global Rank (Amazon)
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About Me

AG Abhilash Ganji
Rank #1 Globally AI Competition — 4500+ Amazon Engineers
National Winner IIM Bangalore Case Study Competition
National Racer Finalist Men's 200cc Category

Building AI systems that don't just work in notebooks — they work in production.

I'm Abhilash Ganji, a Senior Data Science Engineer at EPAM Systems in Hyderabad and a former Amazon engineer. Over the past 7+ years, I've designed and deployed machine learning systems that operate at massive scale — from recommendation engines serving millions of users to transformer-based NLP models predicting business-critical outcomes.

My work sits at the intersection of research and engineering. I don't just build models — I build complete ML systems: data pipelines, training infrastructure, serving layers, and monitoring. Whether it's fine-tuning LLMs, architecting recommender systems, or building forecasting engines, I obsess over taking AI from prototype to production.

"The best AI systems are invisible — they just make things work better. My philosophy is to build AI that's reliable, scalable, and creates measurable impact. Not impressive demos, but systems that survive Monday morning traffic."

Beyond engineering, I'm a national-level racer and competition winner at IIM Bangalore — I bring the same competitive intensity and precision to every ML problem I tackle.

My Journey

Present Senior Data Science Engineer — EPAM

Leading ML initiatives, LLM-based solutions, and Agentic AI systems

Previous Engineer — Amazon

Built large-scale recommender systems and ML pipelines on AWS

Foundation Early Career

Deep learning research, computer vision, and NLP foundations

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Technical Arsenal

Technologies and domains I've mastered over 7+ years of building production AI systems

Machine Learning

End-to-end ML pipeline design, feature engineering, model optimization, A/B testing, and deployment at scale

Scikit-learnXGBoostLightGBMMLflow

Large Language Models

Fine-tuning, prompt engineering, RAG systems, LLM evaluation, and production LLM serving infrastructure

GPTLLaMALangChainHugging Face

Agentic AI

Multi-agent orchestration, tool-use frameworks, autonomous reasoning chains, and agentic workflows

AutoGenCrewAILangGraphOpenAI Agents

Deep Learning

Neural architectures, transformers, CNNs, RNNs, attention mechanisms

PyTorchTensorFlowKeras

NLP

Text classification, NER, sentiment analysis, topic modeling, BERT fine-tuning

BERTspaCyNLTKTransformers

Recommender Systems

Collaborative filtering, content-based, hybrid systems, personalization at scale

Matrix FactorizationDeep RecSysA/B Testing

Computer Vision

Image classification, object detection, segmentation, OCR pipelines

OpenCVYOLOResNetViT

Data Engineering

ETL pipelines, data warehousing, stream processing, data quality frameworks

SparkAirflowSQLKafka

Cloud & AWS

SageMaker, Lambda, S3, EC2, Step Functions, ML infrastructure on AWS

SageMakerLambdaEC2S3

Snowflake

Data warehousing, Snowpark, ML feature stores, analytics engineering

SnowparkSQLdbtStreams
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Featured Projects

Production-grade ML systems I've designed, built, and deployed

NLP / Transformers

Employee Attrition Prediction Using Transformer NLP

Leveraged transformer-based models (BERT) to analyze employee feedback text data and predict attrition risk with 94% accuracy. Built end-to-end pipeline from data ingestion to real-time inference API serving predictions to HR teams.

Architecture
Data Lake BERT Fine-tune SageMaker REST API
PythonBERTPyTorch HuggingFaceAWS SageMakerFastAPI
Source Code
Recommender Systems

Large-Scale Personalised Recommender System

Built a personalised offer recommendation engine for restaurant customers processing millions of user interactions. Implemented hybrid collaborative filtering + deep learning approach achieving 35% improvement in offer redemption rates.

Architecture
Event Stream Feature Store Deep RecSys Serving Layer
PythonTensorFlowSpark AirflowRedisAWS
Source Code
Bayesian Forecasting

Bayesian Forecasting System for Sales Prediction

Designed a probabilistic forecasting system using PyMC Bayesian models for sales prediction with uncertainty quantification. Enabled business stakeholders to make data-driven inventory decisions with confidence intervals, reducing stockouts by 28%.

Architecture
Snowflake PyMC Model MLflow Dashboard
PythonPyMCSnowflake MLflowPlotlyDocker
Source Code
LLMs / NLP

Sentiment Analysis Engine Using LLMs

Built a multi-lingual sentiment analysis system leveraging Large Language Models with fine-tuning and prompt engineering. Processes customer reviews at scale with nuanced aspect-level sentiment detection and real-time classification.

Architecture
Kafka Stream LLM Pipeline Vector DB Analytics
PythonGPT-4LangChain PineconeFastAPIDocker
Source Code
Anomaly Detection

Real-Time Anomaly Detection System

Developed a multi-variate anomaly detection system using autoencoders and isolation forests for real-time monitoring of production metrics. Detects data drift, system anomalies, and fraud patterns with sub-second latency.

Architecture
Kinesis Autoencoder Alerting Dashboard
PythonPyTorchKinesis LambdaGrafanaDocker
Source Code
View All Projects on GitHub
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Research & Experiments

Deep dives into AI research, experiments, and technical explorations

Experiment

Transformer Architectures for Tabular Data

Exploring how attention mechanisms can outperform traditional gradient boosting methods on structured enterprise data

2024 Deep Learning
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Article

Scaling Recommender Systems: From Matrix Factorization to Deep Learning

A comprehensive analysis of recommendation architecture evolution and practical deployment considerations

2024 RecSys
Read More
Experiment

Bayesian vs Frequentist Approaches in Sales Forecasting

Comparative study of uncertainty quantification methods and their impact on business decision-making

2024 Forecasting
Read More
Article

Building Production-Ready RAG Systems: Lessons Learned

Practical insights from deploying retrieval-augmented generation systems in enterprise environments

2025 LLMs
Read More
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Technical Blog

Deep technical writings on AI, ML system design, and engineering

LLMs
Jan 2025 12 min read

Demystifying LLM Architecture: From Attention to Production

A deep dive into transformer architecture internals — multi-head attention, positional encoding, KV-cache optimization, and what it takes to serve LLMs at scale.

Read Article
Dec 2024 10 min read

Building Recommendation Systems That Actually Scale

From collaborative filtering to deep learning recommenders — real-world architecture patterns for serving millions of users.

Read Article
Nov 2024 8 min read

Probabilistic Forecasting with PyMC: A Practitioner's Guide

Why Bayesian forecasting wins in business settings — uncertainty quantification, prior selection, and real deployment.

Read Article
Oct 2024 15 min read

ML System Design: Patterns I've Used at Amazon Scale

Battle-tested patterns for designing ML systems — feature stores, model serving, monitoring, and the infrastructure that keeps it all running.

Read Article
View All Articles

Download My Resume

Get a comprehensive overview of my experience, skills, and achievements

Download PDF
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Let's Connect

Open to opportunities, collaborations, and interesting conversations about AI

Location

Hyderabad, India