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I'm Mani Chandana Alle, an AI/ML Engineer with nearly four years of experience designing, developing, and deploying scalable ML and NLP solutions across fintech and mobility. I specialize in transformer-based models, LLM fine-tuning, real-time data pipelines, and cloud deployment to deliver actionable insights and robust, compliant AI systems. I thrive in cross-functional teams, build end-to-end pipelines, and create production-grade dashboards and RAG pipelines that empower decision-makers while maintaining HIPAA and SOC 2 standards.…I'm Mani Chandana Alle, an AI/ML Engineer with nearly four years of experience designing, developing, and deploying scalable ML and NLP solutions across fintech and mobility. I specialize in transformer-based models, LLM fine-tuning, real-time data pipelines, and cloud deployment to deliver actionable insights and robust, compliant AI systems. I thrive in cross-functional teams, build end-to-end pipelines, and create production-grade dashboards and RAG pipelines that empower decision-makers while maintaining HIPAA and SOC 2 standards.

Mani Chandana Alle

AI Engineer, Data Scientist, Developer, +2

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I'm Mani Chandana Alle, an AI/ML Engineer with nearly four years of experience designing, developing, and deploying scalable ML and NLP solutions across fintech and mobility. I specialize in transformer-based models, LLM fine-tuning, real-time data pipelines, and cloud deployment to deliver actionable insights and robust, compliant AI systems. I thrive in cross-functional teams, build end-to-end pipelines, and create production-grade dashboards and RAG pipelines that empower decision-makers while maintaining HIPAA and SOC 2 standards.…I'm Mani Chandana Alle, an AI/ML Engineer with nearly four years of experience designing, developing, and deploying scalable ML and NLP solutions across fintech and mobility. I specialize in transformer-based models, LLM fine-tuning, real-time data pipelines, and cloud deployment to deliver actionable insights and robust, compliant AI systems. I thrive in cross-functional teams, build end-to-end pipelines, and create production-grade dashboards and RAG pipelines that empower decision-makers while maintaining HIPAA and SOC 2 standards.

Available to hire

I’m Mani Chandana Alle, an AI/ML Engineer with nearly four years of experience designing, developing, and deploying scalable ML and NLP solutions across fintech and mobility. I specialize in transformer-based models, LLM fine-tuning, real-time data pipelines, and cloud deployment to deliver actionable insights and robust, compliant AI systems.

I thrive in cross-functional teams, build end-to-end pipelines, and create production-grade dashboards and RAG pipelines that empower decision-makers while maintaining HIPAA and SOC 2 standards.

Skills

Experience Level

Expert

Expert

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Expert

Work Experience

AI/ML Engineer at Plaid Inc.

June 1, 2024 - Present

Developed Plaid Insights, an AI-powered platform for transaction categorization, anomaly detection, fraud prevention, and personalized financial insights, enabling real-time decision-making for users and partner apps while maintaining high data integrity across multi-institution datasets. Built scalable data ingestion pipelines using Python, SQL, and Plaid APIs to process over 8M transactions, bank statements, and account metadata, normalizing data into PostgreSQL schemas for robust ML workflows. Implemented transformer-based models including DeBERTa-V3 and Longformer for transaction classification, document parsing, and anomaly detection, optimizing tokenization and context windows. Fine-tuned LLMs on anonymized financial data using LoRA and PEFT, containerized with Docker, and deployed on AWS (EC2 and S3) with SOC 2-compliant production AI operations. Developed real-time insights using RAG pipelines with LangChain, FAISS, and AWS Lambda, and built interactive Streamlit dashboards wit

AI/ML Engineer at Uber India

June 1, 2021 - August 1, 2023

Spearheaded the Smart Mobility Insights Platform, delivering real-time, personalized trip and route recommendations, boosting driver utilization and rider satisfaction by 17%. Architected ETL pipelines from Uber trip data, geospatial telemetry, and driver ratings using PySpark and Airflow. Engineered features including one-hot encoding, geospatial clustering, and driver/rider behavior signals to empower operations, pricing, and demand forecasting. Devised ML models (XGBoost, LSTM, ARIMA) to forecast ride demand, predict driver churn, and optimize surge pricing. Refined model performance with Grid Search and Bayesian optimization using time-series and stratified 5-fold validation (RMSE 6.8, Precision@3 80%, AUC 0.89). Deployed models with Docker and Kubernetes within Uber microservices, integrating Kafka for real-time predictions and dynamic pricing, reducing latency by 28% and maintaining 99.5% uptime during peak hours. Automated bi-weekly retraining with Airflow; implemented Prometheu