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I am Anusha Reddy, a GenAI and AI/ML engineer with 9+ years of experience delivering enterprise-grade AI solutions across banking, healthcare, insurance, automotive, and financial services. I specialize in large language models (GPT-4/3.5, LLaMA, Falcon) and in practical techniques such as fine-tuning, prompt tuning, domain adaptation, transfer learning, and RLHF to build scalable GenAI applications. I have led cross-functional teams of 6–8 engineers, designed end-to-end Python-based AI platforms, and built retrieval-augmented generation and hybrid search systems to improve factual grounding and reduce hallucinations. My work spans data engineering, ML pipelines, model deployment, and monitoring across multi-cloud environments, with a strong emphasis on responsible AI, governance, and explainability.…I am Anusha Reddy, a GenAI and AI/ML engineer with 9+ years of experience delivering enterprise-grade AI solutions across banking, healthcare, insurance, automotive, and financial services. I specialize in large language models (GPT-4/3.5, LLaMA, Falcon) and in practical techniques such as fine-tuning, prompt tuning, domain adaptation, transfer learning, and RLHF to build scalable GenAI applications. I have led cross-functional teams of 6–8 engineers, designed end-to-end Python-based AI platforms, and built retrieval-augmented generation and hybrid search systems to improve factual grounding and reduce hallucinations. My work spans data engineering, ML pipelines, model deployment, and monitoring across multi-cloud environments, with a strong emphasis on responsible AI, governance, and explainability.

Anusha Reddy

AI Engineer, Data Scientist, Full Stack Developer, +4

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I am Anusha Reddy, a GenAI and AI/ML engineer with 9+ years of experience delivering enterprise-grade AI solutions across banking, healthcare, insurance, automotive, and financial services. I specialize in large language models (GPT-4/3.5, LLaMA, Falcon) and in practical techniques such as fine-tuning, prompt tuning, domain adaptation, transfer learning, and RLHF to build scalable GenAI applications. I have led cross-functional teams of 6–8 engineers, designed end-to-end Python-based AI platforms, and built retrieval-augmented generation and hybrid search systems to improve factual grounding and reduce hallucinations. My work spans data engineering, ML pipelines, model deployment, and monitoring across multi-cloud environments, with a strong emphasis on responsible AI, governance, and explainability.…I am Anusha Reddy, a GenAI and AI/ML engineer with 9+ years of experience delivering enterprise-grade AI solutions across banking, healthcare, insurance, automotive, and financial services. I specialize in large language models (GPT-4/3.5, LLaMA, Falcon) and in practical techniques such as fine-tuning, prompt tuning, domain adaptation, transfer learning, and RLHF to build scalable GenAI applications. I have led cross-functional teams of 6–8 engineers, designed end-to-end Python-based AI platforms, and built retrieval-augmented generation and hybrid search systems to improve factual grounding and reduce hallucinations. My work spans data engineering, ML pipelines, model deployment, and monitoring across multi-cloud environments, with a strong emphasis on responsible AI, governance, and explainability.

Available to hire

I am Anusha Reddy, a GenAI and AI/ML engineer with 9+ years of experience delivering enterprise-grade AI solutions across banking, healthcare, insurance, automotive, and financial services. I specialize in large language models (GPT-4/3.5, LLaMA, Falcon) and in practical techniques such as fine-tuning, prompt tuning, domain adaptation, transfer learning, and RLHF to build scalable GenAI applications.

I have led cross-functional teams of 6–8 engineers, designed end-to-end Python-based AI platforms, and built retrieval-augmented generation and hybrid search systems to improve factual grounding and reduce hallucinations. My work spans data engineering, ML pipelines, model deployment, and monitoring across multi-cloud environments, with a strong emphasis on responsible AI, governance, and explainability.

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Work Experience

Gen AI Engineer at Molina Healthcare

July 1, 2024 - Present

Built end-to-end LLM-powered document intelligence pipelines on AWS to process clinical notes, EOBs, and insurance claims using Textract OCR, embeddings, and retrieval-augmented generation (RAG), reducing manual review effort by 35%. Engineered secure RAG architectures with FAISS/Pinecone vector stores integrated with S3 and Snowflake for structured healthcare data, improving response accuracy by 22%. Developed scalable GenAI microservices (Python, FastAPI, AWS Lambda, API Gateway) delivering 40% lower inference latency. Integrated LLM workflows with SageMaker, Bedrock, EC2, S3, and CloudWatch to enable end-to-end training, deployment, monitoring, and lifecycle automation. Used Snowflake for storage/feature engineering across structured and semi-structured healthcare data, boosting data processing efficiency by 30%. Built customer-facing AI chatbots for claims troubleshooting and recommendations, improving automated issue resolution by 28%. Implemented MLOps pipelines (CI/CD, MLflow, S

AI/ML Engineer at Bank of America

August 1, 2022 - June 1, 2024

Engineered end-to-end ML pipelines on Azure (Azure ML, Data Factory, ADLS Gen2) for scalable ingestion, training, and inference, reducing model deployment time by 30%. Leveraged Azure OpenAI and Cognitive Services for document classification, text analytics, and intelligent automation, improving processing accuracy by 20% across financial documents. Designed secure AI systems for banking environments with GDPR/CCPA compliance, including encryption and governance. Deployed real-time and batch inference services using AKS, Azure Functions, and REST APIs, achieving 40% faster response times for customer-facing AI services. Implemented MLOps pipelines with Azure DevOps, MLflow, and CI/CD, enabling automated retraining, model versioning, and rollback, improving release reliability by 35%. Built a machine learning–based call quality analytics system using Python and Scikit-learn (Random Forest), increasing quality scoring accuracy by 18%. Developed secure GenAI microservices with Python an

AI-ML Engineer at Nationwide

April 1, 2020 - July 1, 2022

Processed and engineered large-scale structured and unstructured datasets for ML training and analytics, improving model input quality and performance stability by 20%. Designed and optimized ML models (GLMs, Random Forest, Time Series) for classification, regression, and forecasting, boosting predictive accuracy by 18%. Applied Generative AI for ML system monitoring, detecting data drift and anomalies in production models, reducing degradation incidents by 25%. Built NLP solutions including chatbots, summarization, and Q&A using GPT-based architectures, improving automated query resolution by 30%. Implemented Reinforcement Learning-based monitoring to track real-time model accuracy, latency, and throughput, improving inference efficiency by 15%. Developed distributed ML pipelines with PySpark, Spark SQL, Databricks; scaled LLM training and inference using TensorFlow Distributed, PyTorch DDP, and Apache Spark. Deployed ML solutions on GCP (BigQuery, DataProc, GKE, Compute Engine, Cloud

Data Scientist/ ML Engineer at Tesla

October 1, 2018 - March 1, 2020

Designed and deployed machine learning models for predictive maintenance, anomaly detection, and quality analytics across manufacturing and vehicle telemetry data, reducing unplanned downtime by 25%. Built scalable data ingestion and feature engineering pipelines using Python, PySpark, Azure Data Lake, and SQL. Developed time-series forecasting models (LSTM, ARIMA) to predict equipment failures and energy consumption, improving production efficiency by 18%. Implemented computer vision models using CNNs and OpenCV for defect detection, increasing quality control accuracy by 22%. Applied deep learning with TensorFlow and PyTorch for pattern recognition across telemetry and image data; Designed real-time analytics pipelines with Azure Event Hubs, Kafka, Spark Streaming. Deployed ML models as containerized REST APIs using Docker, Kubernetes (AKS), and Azure ML endpoints. Tuning with Grid Search and Bayesian Optimization. Created dashboards in Power BI and Tableau; used Azure ML for trainin

Data Scientist at Broadridge

September 1, 2016 - June 1, 2018

Led end-to-end data science solutioning across customer analytics; built scalable ML pipelines on AWS (ECS, Fargate, EC2, Lambda, S3, DynamoDB); Automated data pipelines with AWS Glue/Data Pipeline and PySpark; Improved churn prediction and customer risk scoring with ensemble methods; Achieved 5–10% performance gains. Built dashboards in Power BI and Tableau to translate ML insights into business KPIs; Implemented production monitoring and governance with CloudWatch/CloudTrail; Reduced model rollout time by 30%.