# Meghana Reddy > Senior AI/ML Engineer | Generative AI, RAG, LLMs, LangChain | Healthcare, Finance & Enterprise AI | Data Scientist | Python, PyTorch, MLOps | Building Real-World AI Systems Location: Chicago, Illinois, United States Profile: https://flows.cv/meghanareddy I’m a Senior AI/ML Engineer with 11+ years of experience building real-world AI and data solutions across healthcare, finance, insurance, and enterprise environments. My work focuses on solving complex business problems using machine learning, data science, and modern Generative AI approaches. Over the years, I’ve designed and deployed production-grade systems using Python, PyTorch, SQL, Spark, and cloud platforms like AWS, covering everything from large-scale data pipelines to model deployment and MLOps workflows. More recently, I’ve been deeply focused on Generative AI, LLMs, NLP, and Retrieval-Augmented Generation (RAG) systems — building intelligent solutions that combine vector search, embeddings, and domain knowledge to deliver reliable, explainable, and trustworthy AI outputs in regulated environments. I’ve built ML solutions for fraud detection, credit risk, healthcare analytics, document intelligence, and large-scale automation — often working closely with domain experts to make sure the technology actually fits real-world workflows. I care a lot about building AI that is not only powerful, but also practical, transparent, and usable. My career goal is to continue building high-impact AI/ML systems that bridge strong engineering with real business value — especially in teams that care about quality, ethics, and long-term impact. Core strengths include: AI/ML Engineering, Data Science, Generative AI, LLMs, RAG, NLP, Python, PyTorch, Spark, SQL, AWS, Data Pipelines, MLOps, and Model Deployment. I’m open to senior opportunities where I can contribute to challenging AI/ML problems and collaborate with strong technical teams. ## Work Experience ### Sr. AI/ML Engineer @ PENNYMAC Jan 2023 – Present | Westlake Village, California, United States • Built GenAI pipelines (GPT-4, Llama, Hugging Face) to extract underwriting, claims, and exposure data from complex insurance documents, reducing manual review time by ~70%. • Designed RAG & Agentic AI systems (Pinecone, Weaviate, LangChain) to ensure LLMs used carrier-approved policy, endorsement, and claims data, cutting hallucinations by 60%+. • Integrated LLM-generated features into XGBoost & LightGBM models to improve fraud detection, claim severity prediction, and underwriting risk scoring. • Deployed FastAPI-based AI platforms with PII/PHI redaction, guardrails, and evaluation metrics, enabling secure real-time policy Q&A, clause checks, and document summarization. ### AI/ML Engineer @ Charter Communications Jan 2022 – Jan 2023 | Stamford, CT Built TensorFlow & PyTorch AI pipelines on DOCSIS, RF, and CMTS telemetry to detect network faults and signal degradation, improving fault-recognition accuracy by 26% across high-density service groups. • Applied BERT & DistilBERT NLP to modem logs, field tickets, and CMTS alerts, enabling automated root-cause detection and faster NOC triage from unstructured operational data. • Developed autoencoders, VAEs, LSTM & attention models to predict node health, detect ingress noise, RF leakage, and plant instability before customer impact. • Deployed real-time AI scoring APIs with SHAP explainability and AWS EMR retraining, enabling proactive, transparent, and scalable network-health monitoring. ### Machine Learning Engineer @ Schonfeld Jan 2020 – Jan 2022 | New York, United States • Built Python, PySpark & scikit-learn ML pipelines to score transactions and detect fraud across card, ACH, and digital channels, improving fraud signal throughput by 28%. • Engineered behavioral, geo-velocity, merchant, and time-window features that significantly improved fraud and credit-risk model accuracy and stability. • Trained and optimized XGBoost, LightGBM, and ensemble models to reduce false negatives and strengthen high-risk transaction detection under strict risk-governance standards. • Delivered real-time scoring & model monitoring (Spark Streaming, MLflow, SHAP, drift detection) enabling fast alerts, explainable decisions, and audit-ready fraud operations. ### Machine Learning Engineer- Healthcare Analytics @ County of Santa Clara Jan 2017 – Jan 2020 | San Jose, California, United States • Built RAG-based semantic search & NLP pipelines to surface insights from public health records, clinical guidelines, and case notes, improving information retrieval speed and accuracy for analysts and care teams. • Developed ML models for population health, SDOH analysis, risk stratification, and member segmentation, enabling data-driven planning for county healthcare programs. • Engineered PySpark & Spark SQL pipelines to process large-scale eligibility, encounter, and utilization data, supporting scalable public-sector healthcare analytics. • Automated reporting and dashboards using Python & Tableau, reducing recurring analytics turnaround time by 30%+ while ensuring responsible and explainable AI. ### Data Scientist @ Equifax Jan 2016 – Jan 2017 | Atlanta, Georgia, United States • Built credit risk & default prediction models using Python, SQL, and scikit-learn on bureau tradelines, utilization, and delinquency data, improving model accuracy by 18% across Equifax’s risk-scoring systems. • Engineered Spark, PySpark & Hive feature pipelines to create stable, interpretable credit behavior variables for fraud, risk, and creditworthiness modeling. • Leveraged AWS EMR, S3, Hadoop & Spark to process high-volume bureau data, enabling scalable training, validation, and batch scoring for enterprise credit models. • Implemented model validation, drift monitoring (PSI/KS), and compliance-ready deployments, ensuring reliable and audit-ready credit decisioning for financial institutions. ### Data Engineer @ IBM Jan 2014 – Jan 2016 | Mumbai, Maharashtra, India • Built SSIS & SQL Server ETL pipelines for pharmacy, claims, and EHR data, improving data processing performance by 30% while ensuring reliable, enterprise-grade ingestion. • Designed healthcare data warehouses aligned to clinical and pharmacy workflows, enabling accurate regulatory reporting and analytics-ready datasets. • Implemented HIPAA & PHI compliance controls (masking, encryption, auditing, validation) to ensure secure handling of sensitive healthcare data across all ETL layers. • Automated data quality, audits, and performance tuning, reducing manual QC by 40% and accelerating analytics and ML-ready data delivery. ## Contact & Social - LinkedIn: https://linkedin.com/in/meghanar07 --- Source: https://flows.cv/meghanareddy JSON Resume: https://flows.cv/meghanareddy/resume.json Last updated: 2026-04-18