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Meghana Reddy

Senior Machine Learning Engineer

Hyderabad, India • +91 98495 60277 • meghana.reddy@hey.com • linkedin.com/in/meghana-reddy-ml • github.com/meghanareddy

Portfolio: www.eliorexa.com/portfolio/ai-ml-engineer-mid

Professional Summary

Machine learning engineer with 5 years taking models from notebook to production in e-commerce and fintech. Owns the full lifecycle — data pipelines, PyTorch/TensorFlow training, model deployment and MLOps — and tracks success in the numbers that matter: accuracy, latency, cost and revenue. Shipped LLM and NLP systems serving millions of requests, built the MLOps backbone teams rely on, and mentors junior engineers on experiment discipline.

Skills

ML & deep learning: PyTorch, TensorFlow, scikit-learn, Hugging Face, XGBoost, LightGBM

LLMs & NLP: LLM fine-tuning, RAG, Embeddings & vector search, LangChain, Named-entity recognition, Evaluation harnesses

Data & MLOps: Airflow, Spark, Feature stores (Feast), MLflow, Kubeflow, Docker, Kubernetes

Cloud & serving: AWS (SageMaker, S3, Lambda), GCP (Vertex AI, BigQuery), Triton, FastAPI, Terraform, Datadog

Core Competencies

Python · PyTorch · TensorFlow · scikit-learn · LLMs · NLP · MLOps · data pipelines · model deployment · AWS · GCP · recommender systems · mentoring

Work Experience

Machine Learning Engineer — Kartmint (Series C e-commerce)

Aug 2022 – Present

Hyderabad

  • Own the product-recommendation system (PyTorch two-tower model) serving 9M users; lifted click-through 22% and add-to-cart 11% over the prior heuristics.
  • Built a real-time inference service on AWS SageMaker + Triton holding p99 latency under 80ms at 4,000 requests/second during peak sale traffic.
  • Designed the Airflow + Spark feature pipeline feeding a Feast feature store, cutting feature-freshness lag from 24 hours to 15 minutes.
  • Shipped an LLM-powered support-triage classifier (fine-tuned transformer) that auto-routes 68% of tickets, saving the CX team ~₹40L/year in handling cost.
  • Standardized MLOps with MLflow and Kubeflow pipelines, cutting model-to-production time from 3 weeks to 4 days and mentoring 2 junior engineers on it.

Data Scientist — PaySprout

Jul 2020 – Jul 2022

Bengaluru

  • Built a fraud-detection model (XGBoost + scikit-learn) on 12M transactions that cut fraud losses 31% while holding false positives under 0.4%.
  • Deployed batch and streaming scoring on GCP Vertex AI with automated drift monitoring, catching 3 silent data-quality regressions before they hit revenue.
  • Reduced training cost ~₹5.5L/year by moving feature computation to BigQuery and right-sizing GPU jobs.

Projects

EvalForge — open-source LLM eval harness — 5 features gated

A pytest-style evaluation framework for LLM and RAG pipelines.

  • Authored a configurable harness scoring relevance, faithfulness and latency; adopted internally to gate 5 LLM features before release.

Tech: LLMs, Python, Evaluation

Interactive 3D Portfolio

WebGL portfolio built on Eliorexa, linked from this résumé.

  • Reactive three.js hero, scroll-driven case studies, lazy-loaded and reduced-motion safe — LCP 1.2s, CLS 0.02 on mobile.

Tech: three.js, React, Performance

Education

M.Tech Computer Science (Machine Learning)

2020

IIT Hyderabad, Hyderabad

Thesis: low-latency neural ranking for recommender systems

Certifications

  • AWS Certified Machine Learning – Specialty — Amazon Web Services (2023)
  • Google Cloud Professional ML Engineer — Google Cloud (2022)