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harshitha - Certfied GEN AI engineer

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Location: Charlotte, North Carolina, USA

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SAI HARSHITHA PADALA [email protected] || +1-980-352-6677|| https://www.linkedin.com/in/psharshitha/ PROFESSIONAL SUMMARY Data Scientist with 9+ years of experience designing and deploying AI and Generative AI solutions for large-scale healthcare workflows. Strong hands-on expertise in Python for model development, data processing, and end-to-end AI pipeline automation. Extensive experience building and optimizing deep-learning models using TensorFlow and PyTorch. Developed and customized LLM-based solutions for clinical summarization, document understanding, and healthcare analytics. Designed retrieval-augmented generation (RAG) pipelines integrating embeddings, vector search, and medical context retrieval. Worked with Azure AI services for hosting LLM inference, orchestrating workflows, and deploying ML components at scale. Built embedding pipelines using transformer encoders to support RAG, search, and retrieval-focused applications. Strong foundation in NLP covering text classification, summarization, semantic search, and transformer-based modeling. Applied fine-tuning and optimization strategies to improve model accuracy for healthcare-specific tasks. Experienced converting unstructured clinical and payer data into usable features for downstream ML and GenAI systems. Implemented evaluation strategies including model validation, grounding checks, and performance benchmarking. Supported healthcare decision-making by developing AI components that improved automation and reduced Manual review. Worked end-to-end across data ingestion, model development, deployment, monitoring, and continuous optimization on Azure. CERTIFICATIONS Certified Oracle Cloud Infrastructure 2025 Certified Generative AI Professional LINK Coursera- Data Science Professional Certificate- IBM LINK TECHNICAL SKILLS Generative AI LLMs, Generative AI Pipelines, RAG (Retrieval-Augmented Generation), Fine-Tuning, Prompt Engineering, Model Guardrails, Structured Outputs Deep Learning & ML TensorFlow, PyTorch, Scikit-Learn, Neural Networks, Embeddings, Text Classification, Summarization, Transformer Models Azure AI Ecosystem Azure OpenAI, Azure Cognitive Search, Azure Functions, Azure Kubernetes Service (AKS), Azure Container Registry, Azure Storage, Azure SQL RAG & Retrieval Systems Embedding Generation, Vector Search, FAISS-style Indexing, Document Chunking, Context Augmentation, Retrieval Orchestration NLP Tokenization, Named Entity Extraction, Medical Text Processing, Semantic Search, Domain-Specific Text Cleaning Model Development & Optimization Model Training, Fine-Tuning, Evaluation Metrics, Hyperparameter Tuning, Drift Detection, Output Validation Python Engineering Python, FastAPI, Async Workflows, REST APIs, JSON/Parsers, Modular AI Components, Pipeline Automation Healthcare Data Claims Data, Clinical Documents, EHR/Provider Notes, HIPAA-Compliant Data Workflows Programming & Frameworks Python, FastAPI, Async I/O, PyTorch, TensorFlow, Scikit-Learn, REST APIs, JSON/Parsers Tools & Platforms Git, CI/CD, Jupyter, VS Code, Postman, MLflow (tracking), Application Insights PROFESSIONAL EXPERIENCE Client: Optum , Chicago, IL Role: Data Scientist-Gen AI Engineer Duration: October 2023 Present Project Scope: Built Azure-based GenAI solutions using LLMs and RAG pipelines, and I developed models in Python using frameworks like TensorFlow and PyTorch to support healthcare data workflows. My focus was on turning unstructured clinical documents into accurate, retrieval-enhanced outputs for care management and analytics. Developed AI and ML models using Python, TensorFlow, and PyTorch to support clinical analytics, risk prediction, and operational workflows. Designed and implemented LLM-based features on Azure, including medical summarization, eligibility interpretation, and document understanding. Built end-to-end RAG pipelines using embedding generation, vector indexing, and retrieval layers optimized for healthcare content. Preprocessed and transformed unstructured clinical documents using tokenization, normalization, and embedding-based text pipelines. Fine-tuned LLMs for domain-specific tasks involving claims narratives, care management notes, and provider documentation. Developed scalable inference services on Azure using containerized deployments and model-serving best practices. Integrated LLM outputs into clinical decision workflows using REST APIs and structured response templates. Automated feature extraction from lab results, progress notes, and medical histories for downstream ML applications. Engineered embeddings for clinical terminology, ICD/CPT codes, and payer rules to improve retrieval and contextual reasoning. Optimized model performance through hyperparameter tuning, evaluation metrics, and error analysis. Implemented data pipelines for ingesting, cleaning, and validating healthcare datasets from claims, EHR, and provider systems. Developed RAG orchestrations that combined retrieval, model reasoning, and prompt routing for high-accuracy responses. Built monitoring checks for drift, latency, and inference reliability across Azure-hosted GenAI services. Collaborated with clinical SMEs to align model outputs with medical guidelines and payer policies. Integrated Azure Cognitive Search and vector-based retrieval to enhance LLM contextual grounding. Worked with secure healthcare data under HIPAA guidelines, ensuring compliance in all AI workflows. Created reusable AI components and Python utilities that standardized training, inference, and evaluation across teams. Implemented prompt-engineering strategies, including few-shot templates and medical context prompts, to improve LLM accuracy on clinical and payer tasks. Environment: Azure, Python, TensorFlow, PyTorch, Azure OpenAI, Azure Cognitive Search, Hugging Face, RAG pipelines, vector embeddings, REST APIs, Git, CI/CD, healthcare claims and clinical documents. Client: Spencer Health Solutions, Morrisville, NC Role: Data Scientist Duration: December 2021 July 2023 Project Scope: Built ML pipelines on AWS SageMaker to predict member adherence, risk scores, and payer cost drivers using structured claims, enrollment, and pharmacy data.Developed an early RAG-style retrieval workflow using S3 + Athena + embeddings to pull historical claims, formulary rules, and provider notes for analytics use cases. Developed ML models for risk scoring, adherence prediction, and member stratification using Python, scikit-learn, and AWS SageMaker distributed training. Created ETL pipelines using AWS Glue + Lambda + Athena to standardize claims, pharmacy fills, encounter data, and eligibility files. Implemented an early RAG workflow where embeddings stored in S3 retrieved clinical and claims snippets to support analytics interpretation. Built feature engineering scripts to derive chronic-condition flags, episode-of-care timelines, utilization frequencies, and medication adherence metrics. Designed SageMaker inference endpoints to deploy models for real-time payer analytics dashboards. Integrated formulary rules, provider network metadata, and medication-tier information into model inputs for more accurate payer predictions. Automated dataset refresh cycles using Step Functions for claims, provider directories, medication lists, and historical outcomes. Developed PyTorch-based sequence models to analyze refill patterns, gaps in therapy, and multi-drug compliance behaviors. Built Athena queries to process millions of claims records, mapping CPT/HCPCS codes to cost drivers and UM decision variables. Implemented explainability using SHAP/LIME for model transparency across UM and care-management teams. Prepared model validation reports aligned with payer accuracy, fairness, and audit requirements. Collaborated with pharmacists, clinical analysts, and data engineers to test adherence-prediction outputs and ensure trust in the model. Optimized pipeline performance by migrating heavy queries to Glue Spark jobs for large historical claims processing. Created S3-based embedding stores for retrieving prior cases, formulary exceptions, and provider patterns. Supported internal analytics teams with Python utilities for data cleaning, ICD/CPT grouping, and time-bound patient history extraction. Environment:Python, AWS SageMaker, AWS Glue, Athena, S3, PyTorch, XGBoost, LightGBM, Docker, Boto3, ICD-10/CPT/HCPCS, claims & pharmacy datasets. Client: USCC Chicago , Illinois USA Role: Data Scientist -Machine Learning Engineer Duration: December 2019 November 2021 Project Scope: Developed machine learning and PySpark workflows to solve key telecom business problems predicting customer churn, improving retention strategies, and optimizing revenue across subscriber segments. Developed end-to-end churn forecasting pipelines using PySpark on AWS EMR, integrating daily subscriber activity, billing records, and call center interactions into ML-ready datasets. Built and maintained PySpark-based ETL pipelines to process customer usage, billing, and interaction data for downstream predictive modeling. Developed machine learning models for churn prediction, customer segmentation, and ARPU forecasting using Python (scikit-learn, TensorFlow). Designed feature stores and transformation logic in SQL to standardize data inputs for model training and validation. Automated data extraction, preprocessing, and scoring workflows using Airflow DAGs and shell scripts to ensure repeatable production runs. Implemented model monitoring and retraining triggers based on data drift and performance degradation using Python-based automation. Collaborated with marketing and operations teams to translate predictive insights into retention strategies and campaign targeting. Deployed models as RESTful APIs using Flask and Docker, integrating outputs with internal analytics dashboards Performed hyperparameter tuning, cross-validation, and model explainability studies to improve prediction accuracy and transparency. Supported migration of analytical workloads from on-prem Hadoop to early Databricks and cloud-based infrastructure for scalability and maintainability. Environment: Snowflake, PySpark, AWS EMR, SageMaker, Redshift, Lambda, Step Functions, SQL, Python, scikit-learn, XGBoost, TensorFlow, Tableau, QuickSight, GitHub, AWS Glue, Confluence Client: Cygnet Infotech, Hyderabad , India Role: Data Analyst Duration: June 2016 September 2019 Project Scope: Built analytics dashboards and automated reporting workflows to solve real customer-service challenges tracking SLAs, reducing escalations, and improving NPS/CSAT insights for operations teams. Analyzed customer-service and product-usage data to identify trends in resolution times, escalation rates, and recurring issue categories for performance optimization Created interactive Power BI and QlikView dashboards tracking SLA compliance, customer-satisfaction metrics, and agent-level performance KPIs used by operations leadership Developed Excel-based reconciliation reports to track monthly billing discrepancies, refunds, and invoice-level anomalies, ensuring financial accuracy and transparency Collaborated with business teams to define KPI logic and automated weekly / monthly reporting using SQL queries and Excel macros, improving report turnaround time Built user-friendly Excel dashboards with PivotTables, slicers, and conditional formatting to help non-technical stakeholders filter data by region, agent, or issue type. Partnered with QA and product teams to categorize issues by severity and frequency, helping prioritize bug fixes and product enhancements Designed scorecards and ranking charts to visualize weekly NPS, CSAT, and agent-level satisfaction metrics for performance reviews Supported quarterly business reviews by preparing trend analyses and visual summaries of performance metrics, customer feedback, and SLA attainment Environment: SQL, Power BI, QlikView, Excel, PivotTables, VLOOKUP, Excel Macros, Slicers, Conditional Formatting, Customer Support KPIs, NPS, CSAT, SLA Metrics EDUCATION Bachelor of Technology (B. Tech) in Information Technology KLUniversity May 2016 Vijayawada, Andhra Pradesh, India Keywords: continuous integration continuous deployment quality analyst artificial intelligence machine learning business intelligence sthree Illinois North Carolina