# Rishabh Sharma > AI/ML Engineer | Deep Learning | NLP | Big Data | AWS | Python | TensorFlow | Scalable ML Solutions | Cloud & Distributed Systems Location: Dallas, Texas, United States Profile: https://flows.cv/rishabhsharma1 AI/ML Engineer with 4+ years of experience building scalable machine learning and generative AI solutions across cloud environments. Skilled in developing end-to-end ML pipelines, deep learning models, and NLP systems using Python, TensorFlow, PyTorch, and transformer-based architectures. Experienced in designing LLM-powered applications including retrieval-augmented generation (RAG), document intelligence, and conversational AI systems. Proficient in deploying production-grade AI solutions using MLOps practices, containerization, and cloud platforms such as AWS and Google Cloud. Strong background in data engineering, model optimization, and building scalable AI systems for real-world enterprise applications. ## Work Experience ### AI/ML Engineer @ Quantiphi Jan 2025 – Present | United States • Designed and deployed an end-to-end GenAI-powered document intelligence platform on Google Cloud Platform to process large-scale Medicaid forms, leveraging Document AI, LLM-based entity extraction, and NLP pipelines for automated document understanding. • Built LLM-driven entity extraction pipelines using Gemini foundational and pro models, applying prompt engineering, contextual parsing, and schema-based extraction to improve structured data retrieval from complex healthcare documents. • Developed custom NLP models and document parsing workflows to handle multi-format Medicaid forms, improving extraction accuracy through iterative training, evaluation, and fine-tuning on production datasets. • Implemented Retrieval-Augmented Generation (RAG) architecture to enhance document understanding by integrating structured knowledge sources with LLM outputs, improving response reliability and contextual accuracy. • Architected scalable serverless AI pipelines using Cloud Run, Docker, and GCP services, enabling automatic scaling low-latency processing, and high availability for document processing workloads. • Engineered dynamic request orchestration that intelligently switches between synchronous and asynchronous API processing based on document size and workload patterns, ensuring optimal throughput during peak traffic conditions. • Built production-grade ETL and data processing pipelines handling 3000+ daily document processing events, integrating document ingestion, LLM inference, validation, and structured output generation. • Implemented MLOps practices including model monitoring, performance evaluation, version control, and automated pipeline orchestration for reliable production deployments. • Optimized system performance by tuning container concurrency, CPU/memory allocation, and request batching, improving pipeline efficiency and reducing operational costs. • Collaborated with cross-functional teams to integrate AI-driven document processing workflows. ### AI Engineer @ Wells Fargo Jan 2025 – Jan 2025 | United States • Designed and developed deep learning–based recommendation systems using RNN, CNN, and Transformer architectures in TensorFlow and PyTorch to deliver personalized recommendations and improve content relevance. • Built scalable data preprocessing and feature engineering pipelines using Python (Pandas, NumPy, Scikit-learn) to process large-scale structured and unstructured datasets for machine learning workflows. • Implemented machine learning pipelines for recommendation and predictive analytics, leveraging ensemble learning techniques and gradient boosting models to enhance recommendation stability and model robustness. • Applied advanced Natural Language Processing (NLP) techniques using BERT, Word2Vec, and transformer-based architectures for text classification, semantic analysis, and contextual understanding of customer interaction data. • Integrated Large Language Models (LLMs) using OpenAI APIs and transformer-based models to enable intelligent document understanding, automated text generation, and contextual transaction analysis. • Developed Retrieval-Augmented Generation (RAG) workflows combining semantic embeddings and contextual retrieval to enhance LLM responses and knowledge-based recommendations. • Implemented model lifecycle management and experiment tracking using MLflow, enabling version control, reproducibility, and systematic evaluation of machine learning and LLM models. • Designed scalable cloud-based ML infrastructure on AWS, integrating services such as Amazon S3, AWS Glue, Redshift, and Lambda to support distributed data processing and model deployment. • Applied dimensionality reduction techniques including PCA and autoencoders to optimize high-dimensional feature spaces and improve training efficiency in deep learning pipelines. • Built containerized ML deployment pipelines using Docker and Kubernetes, enabling scalable model serving, automated deployment, and efficient resource management in production environments. ### Machine Learning Engineer @ Genpact Jan 2020 – Jan 2022 • Developed machine learning and predictive analytics models using Python, SQL, Scikit-learn, Random Forest, XGBoost, and Gradient Boosting algorithms to support risk forecasting, churn prediction, and business intelligence across financial and operational datasets. • Built end-to-end data science pipelines including data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment for large-scale structured and semi-structured datasets. • Conducted Exploratory Data Analysis (EDA) on large transactional and operational datasets using Python (Pandas, NumPy, Matplotlib, Seaborn) to identify patterns, anomalies, and key drivers influencing business outcomes. • Designed and implemented deep learning models using TensorFlow and PyTorch, applying neural network architectures for predictive modeling, pattern recognition, and complex data relationships. • Built Natural Language Processing (NLP) pipelines using Python, spaCy, NLTK, and transformer-based models for text classification, sentiment analysis, and intent detection across customer communications and compliance documents. • Fine-tuned transformer-based Large Language Models (LLMs) including BERT and GPT architectures to extract contextual insights from financial documents and automate document analysis workflows. • Implemented feature engineering and dimensionality reduction techniques to improve model generalization and handle high-dimensional data across machine learning pipelines. • Designed and automated ETL workflows and data pipelines using SQL, Informatica PowerCenter, and Python, enabling reliable ingestion and transformation of enterprise data into centralized data platforms. • Developed data visualization and analytics dashboards using Power BI, presenting key financial metrics, model insights, and business KPIs for decision-making and performance monitoring. • Implemented model evaluation and monitoring frameworks using standard ML performance. ## Education ### Masters of Science in Computer and Information Sciences University of North Texas ### High school and Intermediate in Maths science computers St. Joseph's Academy, Dehradun ### Bachelor's degree in Computer Science And Engineering Gurunanak Institute Of Technical Campus ## Contact & Social - LinkedIn: https://linkedin.com/in/rishabh-sharma-5454b8156 - GitHub: https://github.com/rishabh-sharma-official --- Source: https://flows.cv/rishabhsharma1 JSON Resume: https://flows.cv/rishabhsharma1/resume.json Last updated: 2026-04-17