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I'm Praneeth Padala, a Gen AI Engineer with 10+ years of experience designing and deploying Generative AI, ML, and data engineering solutions across banking, healthcare, insurance, and e-commerce. I specialize in LLM fine-tuning, prompt engineering, RAG architectures, and scalable production-grade AI systems, delivering enterprise-grade capabilities with robust governance and security. I’ve built end-to-end pipelines—from ingestion and feature engineering to deployment and monitoring—across cloud platforms, with a strong focus on explainability and regulatory compliance. In my work, I design and ship AI-native decision support and agentic systems, including knowledge assistants, chatbots, and document QA tools, leveraging vector databases, hybrid search, and multi-agent orchestration. I collaborate closely with product, data engineering, and security teams to ensure robust, observable, and cost-efficient AI solutions that scale in production while maintaining trust and governance for sensitive domains.…I'm Praneeth Padala, a Gen AI Engineer with 10+ years of experience designing and deploying Generative AI, ML, and data engineering solutions across banking, healthcare, insurance, and e-commerce. I specialize in LLM fine-tuning, prompt engineering, RAG architectures, and scalable production-grade AI systems, delivering enterprise-grade capabilities with robust governance and security. I’ve built end-to-end pipelines—from ingestion and feature engineering to deployment and monitoring—across cloud platforms, with a strong focus on explainability and regulatory compliance. In my work, I design and ship AI-native decision support and agentic systems, including knowledge assistants, chatbots, and document QA tools, leveraging vector databases, hybrid search, and multi-agent orchestration. I collaborate closely with product, data engineering, and security teams to ensure robust, observable, and cost-efficient AI solutions that scale in production while maintaining trust and governance for sensitive domains.

Praneeth Padala

AI Engineer, Data Scientist, Developer, +1

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I'm Praneeth Padala, a Gen AI Engineer with 10+ years of experience designing and deploying Generative AI, ML, and data engineering solutions across banking, healthcare, insurance, and e-commerce. I specialize in LLM fine-tuning, prompt engineering, RAG architectures, and scalable production-grade AI systems, delivering enterprise-grade capabilities with robust governance and security. I’ve built end-to-end pipelines—from ingestion and feature engineering to deployment and monitoring—across cloud platforms, with a strong focus on explainability and regulatory compliance. In my work, I design and ship AI-native decision support and agentic systems, including knowledge assistants, chatbots, and document QA tools, leveraging vector databases, hybrid search, and multi-agent orchestration. I collaborate closely with product, data engineering, and security teams to ensure robust, observable, and cost-efficient AI solutions that scale in production while maintaining trust and governance for sensitive domains.…I'm Praneeth Padala, a Gen AI Engineer with 10+ years of experience designing and deploying Generative AI, ML, and data engineering solutions across banking, healthcare, insurance, and e-commerce. I specialize in LLM fine-tuning, prompt engineering, RAG architectures, and scalable production-grade AI systems, delivering enterprise-grade capabilities with robust governance and security. I’ve built end-to-end pipelines—from ingestion and feature engineering to deployment and monitoring—across cloud platforms, with a strong focus on explainability and regulatory compliance. In my work, I design and ship AI-native decision support and agentic systems, including knowledge assistants, chatbots, and document QA tools, leveraging vector databases, hybrid search, and multi-agent orchestration. I collaborate closely with product, data engineering, and security teams to ensure robust, observable, and cost-efficient AI solutions that scale in production while maintaining trust and governance for sensitive domains.

Available to hire

I’m Praneeth Padala, a Gen AI Engineer with 10+ years of experience designing and deploying Generative AI, ML, and data engineering solutions across banking, healthcare, insurance, and e-commerce. I specialize in LLM fine-tuning, prompt engineering, RAG architectures, and scalable production-grade AI systems, delivering enterprise-grade capabilities with robust governance and security. I’ve built end-to-end pipelines—from ingestion and feature engineering to deployment and monitoring—across cloud platforms, with a strong focus on explainability and regulatory compliance.

In my work, I design and ship AI-native decision support and agentic systems, including knowledge assistants, chatbots, and document QA tools, leveraging vector databases, hybrid search, and multi-agent orchestration. I collaborate closely with product, data engineering, and security teams to ensure robust, observable, and cost-efficient AI solutions that scale in production while maintaining trust and governance for sensitive domains.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Gen AI Engineer at Capital One

August 1, 2024 - Present

Designed and implemented an enterprise-grade Generative AI Knowledge Assistant to support fraud, compliance, and risk teams by retrieving context-aware insights from policies, claims, and transactional document repositories. Built an end-to-end GenAI and agentic architecture spanning ETL pipelines, document embeddings, Retrieval-Augmented Generation (RAG) workflows, LangChain- and LangGraph-based orchestrations, MCP-style tool interfaces, backend APIs, and cloud deployments on AWS, with experiment tracking and governance using Azure ML and Vertex AI. Engineered scalable Python-based microservices to extract, normalize, and preprocess large volumes of regulatory, policy, and claims documents stored in Amazon S3, enabling downstream conversational AI fulfillment services. Processed unstructured data with OCR, tokenization-aware splitting, and metadata enrichment to improve embedding quality and grounding. Performed LoRA / PEFT fine-tuning on transformer models (Llama-3) to boost domain-s

Applied AI Scientist at BCBS

April 1, 2022 - July 1, 2024

Led the development of Clinical Insights and Readmission Prediction Platform leveraging ML, NLP, and DL to identify high-risk patients and optimize care management workflows. Built an end-to-end architecture for data ingestion, feature engineering, model training, inference, and analytics with strict healthcare compliance and governance. Ingested and harmonized multi-source data (EHRs, claims, unstructured clinical text) using Airflow, PySpark, and AWS Glue. Engineered NLP pipelines with BioBERT/BioGPT, spaCy, and transformers for NER and clinical text summarization. Modeled disease progression and readmission risk with LSTM, GRU, and Transformer architectures. Automated prior authorization/classification workflows. Built risk stratification and patient segmentation models with XGBoost, Random Forest, and logistic regression. Deployed Generative AI summarization tools (T5, GPT) for clinician-facing insights. Implemented robust MLOps with MLflow, Docker, and Kubernetes, including drift

ML Engineer at State of Wisconsin

October 1, 2019 - March 1, 2022

Built AI-driven analytics for state-level claims processing, fraud detection, and operational decision-making across AWS and Snowflake environments. Ingested and harmonized large-scale data from state claims systems using REST APIs, AWS Glue, and Airflow; performed PySpark-based transformations; centralized curated datasets in Snowflake for scalable reporting and experimentation. Engineered predictive features for fraud indicators, utilization anomalies, and disease patterns; trained models with XGBoost, LightGBM, Random Forest, and Logistic Regression, with rigorous cross-validation and automated experimentation using MLflow and SageMaker. Deployed models as FastAPI REST services with automated retraining and monitoring; implemented security practices for data handling and governance. Built dashboards in Power BI and Grafana for real-time monitoring of model performance and operational KPIs.

Data Scientist / ML Engineer at Cigna

June 1, 2017 - September 1, 2019

Delivered predictive healthcare analytics platform forecasting disease risk and readmission to support proactive care management. Built end-to-end data pipelines ingesting EHRs, claims, and unstructured text; harmonized data with HIPAA-compliant workflows. Engineered features for patient history, lab results, and temporal signals; evaluated XGBoost, Random Forest, and Logistic Regression, with explainability via SHAP/LIME. Exposed models via REST APIs (Flask) for real-time scoring and clinician dashboards; established automated retraining and governance using Docker/Kubernetes and MLflow. Delivered real-time analytics dashboards to monitor model performance and clinical KPIs; emphasized model interpretability and regulatory compliance.

Data Engineer at Cipla

June 1, 2015 - April 1, 2017

Led end-to-end healthcare analytics modules using Python, Flask, and SQL to enable real-time AI-driven clinical decision support. Built scalable ETL pipelines with Airflow and PySpark to consolidate multi-source EHR, lab, and vitals data into a unified data lake. Trained disease prediction models and developed NLP pipelines using spaCy and BERT for medical entity extraction. Implemented automated retraining and data governance to ensure HIPAA-aligned data handling. Delivered interactive dashboards (Power BI) for risk scoring and longitudinal trends, and containerized ML components for scalable production delivery on AWS.