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I am a Machine Learning Engineer with 3.5+ years of experience, specializing in designing and deploying state-of-the-art sequence models for real-time anomaly detection. I have deep expertise in building LSTM models using PyTorch, optimizing for sub-second inference with ONNX, and ensuring model explainability with SHAP. I am proficient in the full MLOps lifecycle, using MLflow for versioning. I build robust ML pipelines, implement event correlation (Flink/Beam), and output verdicts to GraphQL. I am a collaborative engineer dedicated to producing high-accuracy, production-grade AI features. I have led secure cloud-native deployments and mentored junior data scientists and ML engineers, delivering measurable impact in healthcare and financial services, with a focus on explainability, governance, and scalable analytics.…I am a Machine Learning Engineer with 3.5+ years of experience, specializing in designing and deploying state-of-the-art sequence models for real-time anomaly detection. I have deep expertise in building LSTM models using PyTorch, optimizing for sub-second inference with ONNX, and ensuring model explainability with SHAP. I am proficient in the full MLOps lifecycle, using MLflow for versioning. I build robust ML pipelines, implement event correlation (Flink/Beam), and output verdicts to GraphQL. I am a collaborative engineer dedicated to producing high-accuracy, production-grade AI features. I have led secure cloud-native deployments and mentored junior data scientists and ML engineers, delivering measurable impact in healthcare and financial services, with a focus on explainability, governance, and scalable analytics.

Nerella Yella Reddy

Data Scientist, AI Engineer, AI Developer, +2

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I am a Machine Learning Engineer with 3.5+ years of experience, specializing in designing and deploying state-of-the-art sequence models for real-time anomaly detection. I have deep expertise in building LSTM models using PyTorch, optimizing for sub-second inference with ONNX, and ensuring model explainability with SHAP. I am proficient in the full MLOps lifecycle, using MLflow for versioning. I build robust ML pipelines, implement event correlation (Flink/Beam), and output verdicts to GraphQL. I am a collaborative engineer dedicated to producing high-accuracy, production-grade AI features. I have led secure cloud-native deployments and mentored junior data scientists and ML engineers, delivering measurable impact in healthcare and financial services, with a focus on explainability, governance, and scalable analytics.…I am a Machine Learning Engineer with 3.5+ years of experience, specializing in designing and deploying state-of-the-art sequence models for real-time anomaly detection. I have deep expertise in building LSTM models using PyTorch, optimizing for sub-second inference with ONNX, and ensuring model explainability with SHAP. I am proficient in the full MLOps lifecycle, using MLflow for versioning. I build robust ML pipelines, implement event correlation (Flink/Beam), and output verdicts to GraphQL. I am a collaborative engineer dedicated to producing high-accuracy, production-grade AI features. I have led secure cloud-native deployments and mentored junior data scientists and ML engineers, delivering measurable impact in healthcare and financial services, with a focus on explainability, governance, and scalable analytics.

Available to hire

I am a Machine Learning Engineer with 3.5+ years of experience, specializing in designing and deploying state-of-the-art sequence models for real-time anomaly detection. I have deep expertise in building LSTM models using PyTorch, optimizing for sub-second inference with ONNX, and ensuring model explainability with SHAP. I am proficient in the full MLOps lifecycle, using MLflow for versioning. I build robust ML pipelines, implement event correlation (Flink/Beam), and output verdicts to GraphQL. I am a collaborative engineer dedicated to producing high-accuracy, production-grade AI features.

I have led secure cloud-native deployments and mentored junior data scientists and ML engineers, delivering measurable impact in healthcare and financial services, with a focus on explainability, governance, and scalable analytics.

Skills

Experience Level

Expert

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Intermediate

Work Experience

Data Scientist at Cigna Health

September 1, 2024 - November 5, 2025

Built an anomaly detection engine for care navigation, analyzing process trees and user behaviors to drive 20% lift in member self-service. Automated HIPAA-compliant deployment with MLflow for versioning and Evidently for drift monitoring, ensuring scalable, audit-ready analytics workflows. Built high-performance PostgreSQL and Oracle databases to support claims, eligibility, and coordination workflows with HIPAA-compliant design and optimized query performance. Engineered secure AWS-based data pipelines (Glue, Redshift, S3) for real-time claims analytics, outputting verdicts to GraphQL endpoints. Developed LSTM sequence models in PyTorch (deployed with ONNX) for fraud detection; integrated SHAP for model explainability. Designed event correlation logic using CEP frameworks (Flink/Beam stubs) for readmission scoring, care planning, and NLP-based note summarization. Mentored junior data scientists and ML engineers on CI/CD in ML (MLOps), automated model retraining, and secure deployment

AI/ML Engineer at Mphasis

July 1, 2023 - July 1, 2023

Developed and deployed predictive analytics models using Random Forest and SVM for a leading banking client, improving loan default prediction accuracy by 19% and enabling proactive risk mitigation strategies. Engineered CNN and BERT-based NLP pipelines for sentiment analysis, text summarization, and NER on multi-lingual customer feedback, increasing text classification precision to 94% and enhancing customer experience analytics. Ran cohort analysis to segment retail customers into actionable tiers, targeting offers to high-value groups and lifting campaign ROI by 15%, delivering about $500K in incremental revenue over two quarters. Implemented AWS data pipelines with S3, Glue, EMR PySpark, and SageMaker to process about 20 million image and text records, using Parquet partitioning and Step Functions to reduce training time by 35% while meeting IAM, KMS, and CloudTrail governance. Developed XGBoost-based anomaly detection models, reducing system downtime by 15%, and NLP pipelines usin

AI/ML Engineer at HCL Tech

January 1, 2022 - January 1, 2022

Built and deployed demand forecasting in scikit-learn and XGBoost, ran backtests and selected the lowest MAPE model, improving error by 21% and helping planners rebalance store inventory. Designed an insurance fraud verification flow with OpenCV preprocessing, MITRE-mapped logic, and a TensorFlow check, cutting manual review time 60%. Built automated ETL pipelines for multi-source data (PostgreSQL, MongoDB, flat files) using Azure services, cutting data preparation time from 10 hours to under 3 hours per cycle. Designed and published Power BI dashboards with DAX and Power Query to monitor sales trends, inventory levels, and quality KPIs, enabling leadership to make data-backed decisions in real time. Performed extensive data cleaning, mapping, and quality checks on GDPR/CCPA-regulated datasets, ensuring 100% compliance with client data privacy and retention policies.