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I'm Aravind Reddy Pesaru, a Senior AI/ML Engineer with 12+ years of experience delivering scalable AI/ML solutions across FinTech, Industrial Manufacturing, Telecom, Healthcare, and Retail. I design end-to-end systems—data ingestion, feature engineering, model development, deployment, API integration, and production monitoring—using modern ML engineering practices. I specialize in fraud detection, churn prediction, predictive maintenance, risk modeling, demand forecasting, and recommendation systems. I am proficient in Python, SQL, PySpark, and cloud platforms (AWS, Azure), with hands-on experience in real-time inference, MLOps, model governance, and explainability (SHAP/LIME). I enjoy collaborating with product and risk teams to translate complex ML into business value and mentoring teams toward scalable AI platform architectures.…I'm Aravind Reddy Pesaru, a Senior AI/ML Engineer with 12+ years of experience delivering scalable AI/ML solutions across FinTech, Industrial Manufacturing, Telecom, Healthcare, and Retail. I design end-to-end systems—data ingestion, feature engineering, model development, deployment, API integration, and production monitoring—using modern ML engineering practices. I specialize in fraud detection, churn prediction, predictive maintenance, risk modeling, demand forecasting, and recommendation systems. I am proficient in Python, SQL, PySpark, and cloud platforms (AWS, Azure), with hands-on experience in real-time inference, MLOps, model governance, and explainability (SHAP/LIME). I enjoy collaborating with product and risk teams to translate complex ML into business value and mentoring teams toward scalable AI platform architectures.

Aravind Reddy Pesaru

Data Scientist, AI Engineer, Data Analyst, +4

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I'm Aravind Reddy Pesaru, a Senior AI/ML Engineer with 12+ years of experience delivering scalable AI/ML solutions across FinTech, Industrial Manufacturing, Telecom, Healthcare, and Retail. I design end-to-end systems—data ingestion, feature engineering, model development, deployment, API integration, and production monitoring—using modern ML engineering practices. I specialize in fraud detection, churn prediction, predictive maintenance, risk modeling, demand forecasting, and recommendation systems. I am proficient in Python, SQL, PySpark, and cloud platforms (AWS, Azure), with hands-on experience in real-time inference, MLOps, model governance, and explainability (SHAP/LIME). I enjoy collaborating with product and risk teams to translate complex ML into business value and mentoring teams toward scalable AI platform architectures.…I'm Aravind Reddy Pesaru, a Senior AI/ML Engineer with 12+ years of experience delivering scalable AI/ML solutions across FinTech, Industrial Manufacturing, Telecom, Healthcare, and Retail. I design end-to-end systems—data ingestion, feature engineering, model development, deployment, API integration, and production monitoring—using modern ML engineering practices. I specialize in fraud detection, churn prediction, predictive maintenance, risk modeling, demand forecasting, and recommendation systems. I am proficient in Python, SQL, PySpark, and cloud platforms (AWS, Azure), with hands-on experience in real-time inference, MLOps, model governance, and explainability (SHAP/LIME). I enjoy collaborating with product and risk teams to translate complex ML into business value and mentoring teams toward scalable AI platform architectures.

Available to hire

I’m Aravind Reddy Pesaru, a Senior AI/ML Engineer with 12+ years of experience delivering scalable AI/ML solutions across FinTech, Industrial Manufacturing, Telecom, Healthcare, and Retail. I design end-to-end systems—data ingestion, feature engineering, model development, deployment, API integration, and production monitoring—using modern ML engineering practices.

I specialize in fraud detection, churn prediction, predictive maintenance, risk modeling, demand forecasting, and recommendation systems. I am proficient in Python, SQL, PySpark, and cloud platforms (AWS, Azure), with hands-on experience in real-time inference, MLOps, model governance, and explainability (SHAP/LIME). I enjoy collaborating with product and risk teams to translate complex ML into business value and mentoring teams toward scalable AI platform architectures.

Skills

Experience Level

Expert

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Work Experience

Sr. AI/ML Engineer at Plaid

October 1, 2024 - Present

Spearheaded design and development of advanced AI/ML fraud detection systems using ensemble learning, deep learning architectures, and graph neural networks. Architected real-time decisioning engines with low-latency inference; engineered enterprise-scale feature engineering pipelines and centralized feature stores; developed hybrid ML/rule-based risk engines to ensure regulatory compliance; implemented time-series forecasting and depth-based models for fraud spikes; built synthetic data generation pipelines; led model validation, backtesting, and risk management; established drift detection and automated retraining; optimized inference with compression, batching, caching, and latency tuning; implemented secure, governance-aligned architectures; integrated ML services with microservices via REST/gRPC; collaborated on AI product roadmaps; performed root cause analysis for fraud incidents; implemented fairness-aware frameworks; established model documentation and lifecycle management; pa

Sr. AI/ML Engineer at Siemens

May 1, 2021 - July 1, 2024

Led production-grade AI/ML systems for predictive maintenance of industrial equipment. Engineered data ingestion and feature engineering for IoT sensor streams and SCADA data; developed classification, regression, and time-series forecasting models; implemented real-time data processing with PySpark; designed anomaly detection and digital twin simulations; deployed containerized services with Docker/Kubernetes; integrated AWS/Azure cloud infrastructure; built Power BI/Tableau dashboards; implemented model monitoring, automated retraining, and governance; mentored junior engineers and conducted architecture reviews.

Sr. AI/ML Engineer at AT&T

December 1, 2018 - March 1, 2021

Led end-to-end AI/ML systems for churn prediction, customer lifetime value modeling, fraud detection, and revenue forecasting. Engineered data pipelines and feature engineering for large-scale telecom datasets (CDR, billing, CRM, performance logs); built models using Pandas, NumPy, Scikit-learn, XGBoost; developed automated ML pipelines and experimentation frameworks; deployed models via REST APIs; implemented MLOps practices for training, validation, deployment, and monitoring; applied Explainable AI (SHAP/LIME); built dashboards and reports for business stakeholders; mentored teams and drove cross-functional collaboration.

Data Scientist at Pfizer

November 1, 2016 - January 31, 2018

Developed and deployed ML models for patient risk scoring, clinical trial outcome prediction, and drug response analysis. Performed EDA on clinical/healthcare datasets; built classification, regression, and clustering models; designed data preprocessing pipelines; applied statistical modeling, hypothesis testing, and A/B testing; created interactive dashboards to communicate insights; collaborated with biostatistics, regulatory, and business units; automated model training/validation workflows; ensured HIPAA compliance and governance.

Sr. Data Analyst at Target

October 1, 2014 - June 30, 2016

Analyzed large-scale retail data to identify trends and improve business performance. Designed SQL queries, stored procedures, and performance-optimized scripts; built executive dashboards (Tableau/Power BI); led customer segmentation and basket analysis; conducted forecasting for demand planning and inventory optimization; collaborated with merchandising, marketing, and supply chain; automated recurring reporting and ensured data quality; mentored junior analysts.

Data Analyst at Reliance Jio

February 1, 2012 - July 1, 2014

Analyzed telecom datasets including subscriber usage, CDRs, and network performance; developed SQL queries, views, and reports; built Excel/Tableau/Power BI dashboards; performed data cleansing and validation; conducted customer segmentation and predictive analysis; automated reports and supported data migrations; ensured data governance.