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Hi, I'm Charan Lingala, a machine learning engineer with 4 years of experience building scalable AI/ML solutions across financial services and telecom. I specialize in Generative AI, LLMs, and RAG-based systems, with strong expertise in transformer models, fraud detection, and customer analytics. I routinely build end-to-end ML pipelines using Python, PyTorch, and Scikit-Learn, and deploy production-grade solutions on cloud platforms such as Azure ML, Databricks, and AWS SageMaker using Docker, Kubernetes, and MLOps practices.\n\nI enjoy turning complex data into actionable insights and driving business impact through improved model performance, reduced processing time, and data-driven decision making. I am comfortable collaborating with cross-functional teams and exploring explainable AI tools like SHAP and LIME to boost auditability and trust in models.…Hi, I'm Charan Lingala, a machine learning engineer with 4 years of experience building scalable AI/ML solutions across financial services and telecom. I specialize in Generative AI, LLMs, and RAG-based systems, with strong expertise in transformer models, fraud detection, and customer analytics. I routinely build end-to-end ML pipelines using Python, PyTorch, and Scikit-Learn, and deploy production-grade solutions on cloud platforms such as Azure ML, Databricks, and AWS SageMaker using Docker, Kubernetes, and MLOps practices.\n\nI enjoy turning complex data into actionable insights and driving business impact through improved model performance, reduced processing time, and data-driven decision making. I am comfortable collaborating with cross-functional teams and exploring explainable AI tools like SHAP and LIME to boost auditability and trust in models.

Charan Lingala

AI Engineer, Data Scientist, Full Stack Developer, +2

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Hi, I'm Charan Lingala, a machine learning engineer with 4 years of experience building scalable AI/ML solutions across financial services and telecom. I specialize in Generative AI, LLMs, and RAG-based systems, with strong expertise in transformer models, fraud detection, and customer analytics. I routinely build end-to-end ML pipelines using Python, PyTorch, and Scikit-Learn, and deploy production-grade solutions on cloud platforms such as Azure ML, Databricks, and AWS SageMaker using Docker, Kubernetes, and MLOps practices.\n\nI enjoy turning complex data into actionable insights and driving business impact through improved model performance, reduced processing time, and data-driven decision making. I am comfortable collaborating with cross-functional teams and exploring explainable AI tools like SHAP and LIME to boost auditability and trust in models.…Hi, I'm Charan Lingala, a machine learning engineer with 4 years of experience building scalable AI/ML solutions across financial services and telecom. I specialize in Generative AI, LLMs, and RAG-based systems, with strong expertise in transformer models, fraud detection, and customer analytics. I routinely build end-to-end ML pipelines using Python, PyTorch, and Scikit-Learn, and deploy production-grade solutions on cloud platforms such as Azure ML, Databricks, and AWS SageMaker using Docker, Kubernetes, and MLOps practices.\n\nI enjoy turning complex data into actionable insights and driving business impact through improved model performance, reduced processing time, and data-driven decision making. I am comfortable collaborating with cross-functional teams and exploring explainable AI tools like SHAP and LIME to boost auditability and trust in models.

Available to hire

Hi, I’m Charan Lingala, a machine learning engineer with 4 years of experience building scalable AI/ML solutions across financial services and telecom. I specialize in Generative AI, LLMs, and RAG-based systems, with strong expertise in transformer models, fraud detection, and customer analytics. I routinely build end-to-end ML pipelines using Python, PyTorch, and Scikit-Learn, and deploy production-grade solutions on cloud platforms such as Azure ML, Databricks, and AWS SageMaker using Docker, Kubernetes, and MLOps practices.\n\nI enjoy turning complex data into actionable insights and driving business impact through improved model performance, reduced processing time, and data-driven decision making. I am comfortable collaborating with cross-functional teams and exploring explainable AI tools like SHAP and LIME to boost auditability and trust in models.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Machine Learning Engineer at JPMorgan Chase & Co.

August 1, 2025 - Present

Deployed AI-driven financial analytics on Azure ML and Databricks using transformers, RAG, and Azure OpenAI to automate portfolio and compliance insights, cutting review time by 60%. Built fraud-detection and credit-risk models using XGBoost, PyTorch Lightning, and Azure AutoML, improving precision by 25% with SHAP and LIME explainability. Implemented containerized ML microservices using Docker and Kubernetes on Azure, integrated with Event Hubs and REST APIs for real-time inference (89% uptime). Engineered ETL pipelines with Azure Data Factory and Delta Lake processing 50M+ records daily. Designed an RAG-based Q&A chatbot with LangChain and Gradio, enabling natural-language querying across 200+ datasets. Achieved 60% faster query resolution and 50% higher retrieval precision, saving 10+ hours weekly via Chain-of-Thought and Few-Shot Prompting.

Machine Learning Engineer – Customer Analytics at LTIMindtree

October 1, 2022 - July 31, 2023

Delivered production-grade churn models impacting 1M+ customers, contributing to a 15% reduction in attrition and multi-million-dollar revenue retention. Designed churn models using Python, Scikit-Learn, and XGBoost on large telecom datasets, improving prediction accuracy by 22%. Developed scalable ML workflows and integrated models into AWS SageMaker with GPU acceleration (CUDA), improving training efficiency by 30%. Built end-to-end data preprocessing and feature engineering pipelines (Pandas, SQL) handling missing data, encoding, and behavioral features, reducing manual effort by 40%. Created dashboards (Matplotlib, Seaborn) and documented model outputs to help stakeholders identify churn trends and speed up decision-making by 25%.

Machine Learning Engineer – Risk & Fraud Analytics at HSBC

July 1, 2020 - September 30, 2022

Developed fraud detection and risk scoring models using Python, Scikit-Learn, and XGBoost on transactional banking data, improving fraud detection rate by 20% and reducing false positives by 15%. Engineered real-time data processing pipelines using SQL and distributed data workflows, enabling analysis of high-volume financial transactions with latency reductions around 25%. Applied feature engineering and anomaly detection techniques on transaction patterns to identify high-risk behaviors across multiple fraud scenarios. Collaborated with cross-functional teams to deploy ML models into production with MLOps practices, ensuring reproducibility, monitoring, and integration into risk analytics systems.