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I am an AI Engineer with 4+ years of experience building and deploying scalable, production-grade machine learning and Generative AI solutions across financial and healthcare domains. I design end-to-end AI systems, including feature engineering pipelines, real-time streaming architectures, model training, validation, and cloud deployments on AWS and Azure. I also work with Spark, Kafka, transformer NLP models, and MLOps practices including model versioning, monitoring, and CI/CD automation.…I am an AI Engineer with 4+ years of experience building and deploying scalable, production-grade machine learning and Generative AI solutions across financial and healthcare domains. I design end-to-end AI systems, including feature engineering pipelines, real-time streaming architectures, model training, validation, and cloud deployments on AWS and Azure. I also work with Spark, Kafka, transformer NLP models, and MLOps practices including model versioning, monitoring, and CI/CD automation.

Mallikarjun Rao Chunduru

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I am an AI Engineer with 4+ years of experience building and deploying scalable, production-grade machine learning and Generative AI solutions across financial and healthcare domains. I design end-to-end AI systems, including feature engineering pipelines, real-time streaming architectures, model training, validation, and cloud deployments on AWS and Azure. I also work with Spark, Kafka, transformer NLP models, and MLOps practices including model versioning, monitoring, and CI/CD automation.…I am an AI Engineer with 4+ years of experience building and deploying scalable, production-grade machine learning and Generative AI solutions across financial and healthcare domains. I design end-to-end AI systems, including feature engineering pipelines, real-time streaming architectures, model training, validation, and cloud deployments on AWS and Azure. I also work with Spark, Kafka, transformer NLP models, and MLOps practices including model versioning, monitoring, and CI/CD automation.

Available to hire

I am an AI Engineer with 4+ years of experience building and deploying scalable, production-grade machine learning and Generative AI solutions across financial and healthcare domains.

I design end-to-end AI systems, including feature engineering pipelines, real-time streaming architectures, model training, validation, and cloud deployments on AWS and Azure. I also work with Spark, Kafka, transformer NLP models, and MLOps practices including model versioning, monitoring, and CI/CD automation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI Engineer at Wells Fargo

August 1, 2024 - Present

Developed and deployed large-scale ML models for fraud detection and financial risk prediction, processing 120M+ daily transactions and improving anomaly detection precision by 32%. Built end-to-end AI pipelines for data ingestion, feature engineering, model training, and batch/real-time inference on AWS services (S3, Glue, Redshift), enabling sub-second risk scoring for downstream systems. Migrated legacy rule-based detection to ML-driven architectures, reducing false positives by 28% and increasing investigation efficiency for compliance teams. Designed Kafka-based real-time streaming pipelines to capture payment and settlement events for near real-time decisioning. Optimized Spark feature engineering to reduce training cycles from 3.8 to 2.4 hours. Implemented MLflow-based model registry with staged promotion and drift validation, automated rollback mechanisms, and CI/CD automation with GitHub Actions and Jenkins. Developed LLM-powered document summarization and risk narrative gener

Machine Learning Engineer – Healthcare Analytics at AstraZeneca by Miraicoders

June 1, 2022 - August 1, 2023

Designed predictive analytics models for healthcare datasets (EHR, claims, lab results) with 18-25% lift in model performance. Engineered large-scale feature pipelines using Spark and Hive to process 80M+ clinical records per run, enabling longitudinal patient modeling. Built automated preprocessing for missing values, ICD-10 inconsistencies, and outliers, improving data quality by 34%. Fine-tuned transformer-based NLP models for clinical text classification and entity extraction, improving F1-score by 19%. Developed ML-ready data lakes on AWS (S3, EC2) with HIPAA-aligned access controls, reducing infrastructure costs by 21%. Implemented A/B testing to validate model improvements and deployed interactive Tableau dashboards linking predictions with operational KPIs, driving targeted outreach and increasing patient follow-through by 19%.

Associate Machine Learning Engineer at Mirai Coders Technology

July 1, 2021 - June 1, 2022

Built supervised and unsupervised learning models for operational forecasting and customer behavior analytics, improving production prediction accuracy by 18%. Developed scalable feature engineering pipelines using Apache Spark and Azure Databricks, increasing transformation throughput by 36%. Designed incremental data processing frameworks supporting continuous model retraining, reducing batch window duration from 6.5 hours to under 4 hours. Implemented model monitoring dashboards tracking drift, feature stability, and performance metrics using Power BI and Evidently AI. Created reusable feature stores with versioned datasets to support offline training and online inference. Applied cloud cost optimization strategies including auto-scaling, storage tiering, and compute right-sizing, reducing monthly AI infrastructure costs by 26%.