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AI/ML Engineer with 3+ years of experience designing and delivering machine learning, Generative AI, and analytics solutions in banking and marketing domains. Skilled in Python, SQL, Snowflake, and production-focused ML workflows including fraud analytics, anomaly detection, classification modeling, and model evaluation. Experienced building LLM applications with RAG using Azure OpenAI, LangChain/LangGraph, and Azure AI Search, along with prompt engineering, embeddings/vector search, and evaluation/guardrails. Strong background in scalable data pipelines, BI dashboards (Power BI/Tableau), SHAP-based explainability, and cross-functional delivery with governance and responsible AI practices.…AI/ML Engineer with 3+ years of experience designing and delivering machine learning, Generative AI, and analytics solutions in banking and marketing domains. Skilled in Python, SQL, Snowflake, and production-focused ML workflows including fraud analytics, anomaly detection, classification modeling, and model evaluation. Experienced building LLM applications with RAG using Azure OpenAI, LangChain/LangGraph, and Azure AI Search, along with prompt engineering, embeddings/vector search, and evaluation/guardrails. Strong background in scalable data pipelines, BI dashboards (Power BI/Tableau), SHAP-based explainability, and cross-functional delivery with governance and responsible AI practices.

Akshith Reddy

AI Engineer, Data Scientist, Data Engineer, +2

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AI/ML Engineer with 3+ years of experience designing and delivering machine learning, Generative AI, and analytics solutions in banking and marketing domains. Skilled in Python, SQL, Snowflake, and production-focused ML workflows including fraud analytics, anomaly detection, classification modeling, and model evaluation. Experienced building LLM applications with RAG using Azure OpenAI, LangChain/LangGraph, and Azure AI Search, along with prompt engineering, embeddings/vector search, and evaluation/guardrails. Strong background in scalable data pipelines, BI dashboards (Power BI/Tableau), SHAP-based explainability, and cross-functional delivery with governance and responsible AI practices.…AI/ML Engineer with 3+ years of experience designing and delivering machine learning, Generative AI, and analytics solutions in banking and marketing domains. Skilled in Python, SQL, Snowflake, and production-focused ML workflows including fraud analytics, anomaly detection, classification modeling, and model evaluation. Experienced building LLM applications with RAG using Azure OpenAI, LangChain/LangGraph, and Azure AI Search, along with prompt engineering, embeddings/vector search, and evaluation/guardrails. Strong background in scalable data pipelines, BI dashboards (Power BI/Tableau), SHAP-based explainability, and cross-functional delivery with governance and responsible AI practices.

Available to hire

AI/ML Engineer with 3+ years of experience designing and delivering machine learning, Generative AI, and analytics solutions in banking and marketing domains. Skilled in Python, SQL, Snowflake, and production-focused ML workflows including fraud analytics, anomaly detection, classification modeling, and model evaluation.

Experienced building LLM applications with RAG using Azure OpenAI, LangChain/LangGraph, and Azure AI Search, along with prompt engineering, embeddings/vector search, and evaluation/guardrails. Strong background in scalable data pipelines, BI dashboards (Power BI/Tableau), SHAP-based explainability, and cross-functional delivery with governance and responsible AI practices.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Capital One

July 1, 2025 - Present

Designed and optimized machine learning pipelines using Python, SQL, and Snowflake (Pandas/NumPy) for fraud risk analytics, improving identification of high-risk transaction patterns by 19%. Built fraud alert prioritization classification models using XGBoost and Logistic Regression, reducing false-positive investigations by 12% and improving analyst efficiency. Developed anomaly detection solutions using feature engineering and statistical analysis to identify abnormal customer behavior. Performed data preprocessing, missing value treatment, feature engineering, and model validation; evaluated models with Precision/Recall/F1, ROC-AUC, SHAP, and error analysis to improve explainability and decision support. Implemented RAG workflows using Azure OpenAI, LangChain, LangGraph, Azure AI Search, embeddings, and MCP to improve enterprise knowledge retrieval and analyst productivity, including prompt engineering improvements for response quality and retrieval accuracy. Built Power BI/Tableau

Machine Learning Engineering Intern at Capital One

January 1, 2025 - March 31, 2025

Supported fraud analytics by analyzing transaction datasets using SQL and Snowflake to identify candidate features for ML modeling. Built Python preprocessing notebooks (Pandas/NumPy) for missing value analysis, outlier detection, feature engineering, and class imbalance treatment. Compared Logistic Regression and XGBoost models using Precision/Recall, ROC-AUC, and F1 to improve confidence in model selection by 15%. Assisted with SHAP-based explainability, validation reporting, dashboard development, and sprint documentation for fraud analytics. Collaborated with senior engineers to evaluate experiments using business performance metrics.

Data Analyst at Sage Softtech

April 1, 2021 - July 31, 2023

Analyzed customer, CRM, website, and campaign data using Python, SQL, and Excel to identify behavior patterns and improve lead conversion by 18%. Performed large-scale data cleaning, transformation, validation, profiling, and feature engineering using Pandas and Power Query, increasing reporting accuracy by 16%. Developed interactive Power BI dashboards with DAX for campaign performance, engagement, and conversion tracking. Built classification models (Random Forest, XGBoost) to predict high-intent customers and improve sales prioritization by 21%, using cross-validation, lift analysis, SHAP explainability, and performance evaluation. Automated recurring reporting workflows using Python/SQL and LangChain-powered techniques, reducing manual effort. Presented analytics and ML findings to marketing/product/leadership teams.