Work Experience

Adobe

AI/ML Engineer

Jul 2025 – Present

USA

  • ▸Rolled out a production RAG pipeline combining embeddings and vector search, cutting document retrieval time by 40% while improving answer accuracy for internal knowledge access.
  • ▸Shipped LLM-driven automation features using OpenAI and Hugging Face, reducing manual content work by 45% and improving consistency across high-volume workflows.
  • ▸Built AWS-based ML pipelines with SageMaker and Airflow handling 10M+ records monthly, supporting stable batch and near real-time inference operations.
  • ▸Introduced MLflow tracking for experiments, model versions, and performance monitoring, reducing production failures by ~25% through early drift detection.
  • ▸Built FastAPI-based ML services integrated into Adobe systems, maintaining low latency under high daily request volumes.
  • ▸Collaborated with product and engineering to deliver AI solutions that improved key product metrics by ~20%.
RAG
OpenAI
HuggingFace
AWS SageMaker
Airflow
MLflow
FastAPI

Accenture

AI/ML Engineer

Sep 2020 – Oct 2023

India

  • ▸Delivered ML solutions for customer analytics and forecasting using Python and SQL, improving prediction accuracy by ~18% for enterprise clients.
  • ▸Engineered data pipelines using PySpark and Airflow to process millions of records daily, cutting data preparation time by 35%.
  • ▸Built NLP pipelines using BERT and spaCy for classification and entity extraction, reducing manual document review effort by 40%.
  • ▸Deployed models via Docker and REST APIs for batch and real-time inference, reducing release cycles by ~30% across multiple client environments.
  • ▸Applied feature engineering and tuning using XGBoost and LightGBM, improving model performance by 20% across diverse datasets.
  • ▸Translated business requirements into production-ready ML solutions that improved operational efficiency by ~15%.
Python
PySpark
BERT
spaCy
XGBoost
LightGBM
Docker
Airflow