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Hi, I’m Anushka Yadav, an AI/ML Engineer with 4+ years of experience delivering scalable machine learning and generative AI solutions using LLMs and deep learning techniques. I specialize in building end-to-end AI systems across NLP, computer vision, and predictive modeling, with hands-on deployment in cloud environments. I enjoy turning complex data problems into practical, enterprise-ready solutions that drive measurable business impact. I’ve led projects at Morgan Stanley and BrainByte Infotech, from financial sentiment analysis and fraud/risk modeling to automated regulatory reporting and anomaly detection. I thrive in cross-functional teams, embrace MLOps and CI/CD practices, and love translating business goals into production-grade ML workflows that enable smarter decisions and better outcomes.…Hi, I’m Anushka Yadav, an AI/ML Engineer with 4+ years of experience delivering scalable machine learning and generative AI solutions using LLMs and deep learning techniques. I specialize in building end-to-end AI systems across NLP, computer vision, and predictive modeling, with hands-on deployment in cloud environments. I enjoy turning complex data problems into practical, enterprise-ready solutions that drive measurable business impact. I’ve led projects at Morgan Stanley and BrainByte Infotech, from financial sentiment analysis and fraud/risk modeling to automated regulatory reporting and anomaly detection. I thrive in cross-functional teams, embrace MLOps and CI/CD practices, and love translating business goals into production-grade ML workflows that enable smarter decisions and better outcomes.

Anushka Yadav

AI Engineer, Data Scientist, Developer, +3

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Hi, I’m Anushka Yadav, an AI/ML Engineer with 4+ years of experience delivering scalable machine learning and generative AI solutions using LLMs and deep learning techniques. I specialize in building end-to-end AI systems across NLP, computer vision, and predictive modeling, with hands-on deployment in cloud environments. I enjoy turning complex data problems into practical, enterprise-ready solutions that drive measurable business impact. I’ve led projects at Morgan Stanley and BrainByte Infotech, from financial sentiment analysis and fraud/risk modeling to automated regulatory reporting and anomaly detection. I thrive in cross-functional teams, embrace MLOps and CI/CD practices, and love translating business goals into production-grade ML workflows that enable smarter decisions and better outcomes.…Hi, I’m Anushka Yadav, an AI/ML Engineer with 4+ years of experience delivering scalable machine learning and generative AI solutions using LLMs and deep learning techniques. I specialize in building end-to-end AI systems across NLP, computer vision, and predictive modeling, with hands-on deployment in cloud environments. I enjoy turning complex data problems into practical, enterprise-ready solutions that drive measurable business impact. I’ve led projects at Morgan Stanley and BrainByte Infotech, from financial sentiment analysis and fraud/risk modeling to automated regulatory reporting and anomaly detection. I thrive in cross-functional teams, embrace MLOps and CI/CD practices, and love translating business goals into production-grade ML workflows that enable smarter decisions and better outcomes.

Available to hire

Hi, I’m Anushka Yadav, an AI/ML Engineer with 4+ years of experience delivering scalable machine learning and generative AI solutions using LLMs and deep learning techniques. I specialize in building end-to-end AI systems across NLP, computer vision, and predictive modeling, with hands-on deployment in cloud environments. I enjoy turning complex data problems into practical, enterprise-ready solutions that drive measurable business impact.

I’ve led projects at Morgan Stanley and BrainByte Infotech, from financial sentiment analysis and fraud/risk modeling to automated regulatory reporting and anomaly detection. I thrive in cross-functional teams, embrace MLOps and CI/CD practices, and love translating business goals into production-grade ML workflows that enable smarter decisions and better outcomes.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/Machine Learning Engineer at Morgan Stanley, USA

January 1, 2024 - Present

Developed and fine-tuned transformer models (FinBERT, GPT-3, LLaMA) for financial sentiment analysis, fraud detection, and risk modeling, achieving an F1-score of 0.92 and enabling real-time analysis of unstructured financial data. Built anomaly detection systems using Autoencoders and Graph Neural Networks, improving fraud detection accuracy to 93% and reducing false positives in compliance monitoring, deployed on AWS SageMaker for real-time inference. Designed retrieval-based AI systems leveraging Retrieval-Augmented Generation (RAG) to automate regulatory reporting and enhance investment research workflows. Implemented end-to-end ML pipelines with MLOps best practices, including CI/CD, model monitoring, and scalable deployments on AWS and Google Cloud AI. Established AI-powered chatbots and voice assistants for wealth management and client services, improving query resolution speed and personalized investment recommendations.

AI/Machine Learning Engineer at Morgan Stanley, USA

January 1, 2024 - Present

Developed and fine-tuned transformer models including FinBERT, GPT-3, and LLaMA for financial sentiment analysis, fraud detection, and risk modeling, achieving an F1 score of 0.92 and enabling real-time analysis of unstructured financial data. Built anomaly detection systems using autoencoders and graph neural networks, improving fraud detection accuracy to 93% and reducing false positives, deployed on AWS SageMaker. Designed retrieval-based AI systems leveraging Retrieval-Augmented Generation (RAG) to automate regulatory reporting and enhance investment research. Implemented end-to-end ML pipelines incorporating MLOps best practices with CI/CD, model monitoring, and scalable deployments on AWS and Google Cloud AI. Established AI-powered chatbots and voice assistants for wealth management improving query resolution and personalized recommendations. Applied reinforcement learning for trading strategy optimization and used time-series forecasting for market prediction. Optimized ML workf

Machine Learning Engineer at BrainByte Infotech, India

July 1, 2021 - October 2, 2025

Crafted customer segmentation models using K-Means and DBSCAN on application usage and transaction data, identifying behavioral clusters that enabled targeted product campaigns and increased engagement ROI. Optimized ML pipelines with PCA for dimensionality reduction and feature engineering, reducing training time by 35%. Enhanced CNN/RNN-based models in TensorFlow to classify support tickets and forecast system failures from time-series logs (92% accuracy). Orchestrated demand forecasting with ARIMA and XGBoost, improving resource planning by 21% and reducing over-provisioning. Built Power BI dashboards for predictive insights, deployed scalable ML pipelines on AKS with Azure Blob Storage for real-time anomaly detection, and reduced cloud costs by 17%. Engineered custom PyTorch models for anomaly detection and performed extensive EDA to improve feature selection and preprocessing efficiency.

Machine Learning Engineer at BrainByte Infotech, India

July 1, 2021 - August 26, 2025

Crafted customer segmentation models using K-Means and DBSCAN on usage and transaction data, improving customer engagement ROI by 15%. Optimized ML pipelines applying PCA for dimensionality reduction and engineered domain-specific features, reducing training time by 35% while maintaining accuracy. Enhanced deep learning models using CNNs and RNNs to classify support tickets and forecast system failures with 92% accuracy enabling automated diagnostics. Developed demand forecasting models using ARIMA and XGBoost, improving resource planning by 21% and reducing cloud service over-provisioning. Created Power BI dashboards for predictive insights to speed stakeholder decision-making. Built scalable ML pipelines on Azure Kubernetes Service integrated with Azure Blob Storage for real-time anomaly detection reducing cloud costs by 17%. Engineered custom PyTorch models for anomaly detection improving accuracy by 18%. Conducted extensive exploratory data analysis to uncover bottlenecks and impro