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I'm a Senior AI/ML Engineer with over 11 years of experience designing, building, and deploying AI, Machine Learning, Deep Learning, and Generative AI solutions across the financial services, healthcare, telecommunications, and IT industries. Throughout my career, I've worked on developing predictive models, enterprise AI applications, intelligent automation solutions, and cloud-native machine learning platforms that solve complex business problems and deliver measurable business value. I have strong expertise in Python, SQL, Scikit-learn, TensorFlow, PyTorch, Spark, and modern Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LangGraph, Agentic AI, and Prompt Engineering. I also have extensive experience building scalable MLOps and LLMOps pipelines using MLflow, Kubeflow, Docker, Kubernetes, CI/CD, and cloud platforms such as AWS and Azure. Over the years, I've developed AI solutions for fraud detection, credit risk modeling, healthcare analytics, customer intelligence, forecasting, recommendation systems, and enterprise automation. I enjoy collaborating with cross-functional teams, mentoring engineers, and delivering production-ready AI solutions that improve business outcomes. I'm passionate about staying current with emerging AI technologies and continuously finding innovative ways to apply them to real-world challenges.…I'm a Senior AI/ML Engineer with over 11 years of experience designing, building, and deploying AI, Machine Learning, Deep Learning, and Generative AI solutions across the financial services, healthcare, telecommunications, and IT industries. Throughout my career, I've worked on developing predictive models, enterprise AI applications, intelligent automation solutions, and cloud-native machine learning platforms that solve complex business problems and deliver measurable business value. I have strong expertise in Python, SQL, Scikit-learn, TensorFlow, PyTorch, Spark, and modern Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LangGraph, Agentic AI, and Prompt Engineering. I also have extensive experience building scalable MLOps and LLMOps pipelines using MLflow, Kubeflow, Docker, Kubernetes, CI/CD, and cloud platforms such as AWS and Azure. Over the years, I've developed AI solutions for fraud detection, credit risk modeling, healthcare analytics, customer intelligence, forecasting, recommendation systems, and enterprise automation. I enjoy collaborating with cross-functional teams, mentoring engineers, and delivering production-ready AI solutions that improve business outcomes. I'm passionate about staying current with emerging AI technologies and continuously finding innovative ways to apply them to real-world challenges.

Anisha Reddy B

Data Scientist, AI Engineer, AI Developer, +2

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I'm a Senior AI/ML Engineer with over 11 years of experience designing, building, and deploying AI, Machine Learning, Deep Learning, and Generative AI solutions across the financial services, healthcare, telecommunications, and IT industries. Throughout my career, I've worked on developing predictive models, enterprise AI applications, intelligent automation solutions, and cloud-native machine learning platforms that solve complex business problems and deliver measurable business value. I have strong expertise in Python, SQL, Scikit-learn, TensorFlow, PyTorch, Spark, and modern Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LangGraph, Agentic AI, and Prompt Engineering. I also have extensive experience building scalable MLOps and LLMOps pipelines using MLflow, Kubeflow, Docker, Kubernetes, CI/CD, and cloud platforms such as AWS and Azure. Over the years, I've developed AI solutions for fraud detection, credit risk modeling, healthcare analytics, customer intelligence, forecasting, recommendation systems, and enterprise automation. I enjoy collaborating with cross-functional teams, mentoring engineers, and delivering production-ready AI solutions that improve business outcomes. I'm passionate about staying current with emerging AI technologies and continuously finding innovative ways to apply them to real-world challenges.…I'm a Senior AI/ML Engineer with over 11 years of experience designing, building, and deploying AI, Machine Learning, Deep Learning, and Generative AI solutions across the financial services, healthcare, telecommunications, and IT industries. Throughout my career, I've worked on developing predictive models, enterprise AI applications, intelligent automation solutions, and cloud-native machine learning platforms that solve complex business problems and deliver measurable business value. I have strong expertise in Python, SQL, Scikit-learn, TensorFlow, PyTorch, Spark, and modern Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LangGraph, Agentic AI, and Prompt Engineering. I also have extensive experience building scalable MLOps and LLMOps pipelines using MLflow, Kubeflow, Docker, Kubernetes, CI/CD, and cloud platforms such as AWS and Azure. Over the years, I've developed AI solutions for fraud detection, credit risk modeling, healthcare analytics, customer intelligence, forecasting, recommendation systems, and enterprise automation. I enjoy collaborating with cross-functional teams, mentoring engineers, and delivering production-ready AI solutions that improve business outcomes. I'm passionate about staying current with emerging AI technologies and continuously finding innovative ways to apply them to real-world challenges.

Available to hire

I’m a Senior AI/ML Engineer with over 11 years of experience designing, building, and deploying AI, Machine Learning, Deep Learning, and Generative AI solutions across the financial services, healthcare, telecommunications, and IT industries. Throughout my career, I’ve worked on developing predictive models, enterprise AI applications, intelligent automation solutions, and cloud-native machine learning platforms that solve complex business problems and deliver measurable business value.

I have strong expertise in Python, SQL, Scikit-learn, TensorFlow, PyTorch, Spark, and modern Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LangGraph, Agentic AI, and Prompt Engineering. I also have extensive experience building scalable MLOps and LLMOps pipelines using MLflow, Kubeflow, Docker, Kubernetes, CI/CD, and cloud platforms such as AWS and Azure.

Over the years, I’ve developed AI solutions for fraud detection, credit risk modeling, healthcare analytics, customer intelligence, forecasting, recommendation systems, and enterprise automation. I enjoy collaborating with cross-functional teams, mentoring engineers, and delivering production-ready AI solutions that improve business outcomes. I’m passionate about staying current with emerging AI technologies and continuously finding innovative ways to apply them to real-world challenges.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

Afar

Advanced

Javanese

Intermediate

Work Experience

Senior AI/ML Engineer at Discover Financial Services

October 1, 2024 - Present

Architected and deployed enterprise-scale AI, ML, DL, and Generative AI solutions using Python, SQL, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, CatBoost, Amazon Bedrock, and LLM frameworks (LangChain, LangGraph, agentic AI). Improved credit risk prediction accuracy by 35% and reduced loan default exposure by 25% using supervised/unsupervised learning, ensemble learning, and Bayesian/statistical modeling. Built fraud detection and AML analytics with anomaly detection and explainability (SHAP/LIME), reducing losses by 28%. Engineered RAG solutions to improve financial document retrieval accuracy by 40%. Implemented scalable feature engineering pipelines with Spark/PySpark and AWS Glue/SageMaker feature store, plus enterprise LLMOps/MLOps using MLflow, Kubeflow, SageMaker Pipelines, model monitoring, drift detection, and responsible AI governance. Built real-time and batch data pipelines with Spark, Kafka, Airflow, and AWS lakehouse components. Developed RESTful inference servi

AI/ML Engineer at Molina healthcare

June 1, 2021 - September 30, 2024

Developed end-to-end AI/ML solutions for healthcare analytics using Python, R, SQL, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, and CatBoost to improve member risk prediction accuracy by 32%. Built predictive models for risk stratification and utilization management using supervised/unsupervised learning, ensembles, classification/regression/clustering, anomaly detection, and statistical modeling; improved early identification of high-risk members for care management programs by 28%. Performed large-scale preprocessing and feature engineering across structured and unstructured datasets using Spark/PySpark and AWS Glue. Built NLP and Generative AI workflows using BERT/Transformers, Hugging Face, spaCy/NLTK, LangChain, LLMs, and RAG with prompt engineering to analyze clinical notes and healthcare documentation. Implemented deep learning models (CNNs/RNNs/LSTMs/Autoencoders/transfer learning) for forecasting and disease prediction, reducing manual review effort by 40%. Streamlin

Senior Data Scientist at Charter Communications

December 1, 2018 - May 31, 2021

Led advanced analytics for telecom datasets in Azure environments covering 10M+ customer records (subscriber usage, billing, service performance, network logs). Designed and deployed churn prediction, retention, and usage forecasting models using Azure ML Studio and Scikit-learn, improving retention targeting by ~15–20% via model-driven segmentation and scoring. Built scalable data pipelines using Azure Data Factory and SQL ETL across multiple enterprise systems. Conducted statistical analysis including regression modeling, hypothesis testing, cohort analysis, A/B testing, and time-series forecasting over 3+ years of telecom behavior data. Implemented classification models (random forest, gradient boosting, logistic regression, SVM, Naive Bayes) within Azure ML workflows for stable churn prediction. Created customer segmentation using K-Means, hierarchical clustering, and PCA, producing 5–8 customer behavior groups. Performed large-scale EDA to identify churn drivers and service bo

Data Scientist at Accenture

April 1, 2017 - August 31, 2018

Developed scalable enterprise solutions using Python, SQL, Pandas/NumPy, and REST APIs to support business applications and distributed systems. Designed and deployed ML-enabled applications using Azure ML Studio and Scikit-learn to automate decision-making and optimize operations. Built enterprise ETL and data integration frameworks using Azure Data Factory, Python, and APIs across multiple applications processing millions of records daily. Implemented analytics applications with regression and time-series forecasting/statistical methods for trend and metric forecasting. Built classification and recommendation/predictive analytics models using decision trees, random forests, logistic regression, SVM, and Scikit-learn. Conducted large-scale analysis with Python for performance bottleneck identification and reliability improvements. Designed centralized database solutions using SQL Server with stored procedures/views/joins/window functions and query optimization. Applied feature enginee

Data Scientist at Sonata Software

July 1, 2015 - March 31, 2017

Designed and developed scalable enterprise software using Python, SQL, OOP, and data structures/algorithms to process large-scale mission-critical business data. Applied Python/SQL/statistical modeling/time-series analysis to identify trends, usage patterns, and operational insights. Built ML-based solutions for forecasting, resource optimization, classification/regression, and automated decision-making using Scikit-learn and predictive modeling. Developed end-to-end ETL and data processing pipelines including validation, feature engineering, normalization, and outlier detection for structured datasets. Automated backend workflows and batch processing using Python scripting, SQL, ETL frameworks, and scheduling to streamline extraction/transformation/integration. Performed EDA to identify anomalies, behavioral trends, and bottlenecks. Built customer analytics and recommendation systems using clustering and data mining. Optimized SQL performance with stored procedures, views, indexing, a