# Anvita Panjugula > AI Scientist @ P&G | A2A | Multi-Agent | RAG | LLM Governance | LangGraph | n8n | LangChain | LlamaIndex | MCP | MLOps | Neo4j | Azure OpenAI | FAISS | Snowflake Cortex | MCP | MLOps | Kafka | Kubernetes Profile: https://flows.cv/anvita I build AI systems that turn messy, ambiguous problems into something people can actually use — and trust. At Procter & Gamble's Digital Accelerator, I architect and ship production GenAI systems: multi-agent workflows, RAG pipelines, LLM-powered automation, and governance infrastructure running on LangChain, LangGraph, LlamaIndex, MCP, Azure OpenAI, and AWS/Kubernetes. A lot of my work lives at the intersection of retrieval quality, observability, and making model behavior explainable to non-technical stakeholders. Before P&G, I built high-throughput scraping and scoring infrastructure across 11K+ sources at Analytics Quad4 and worked as a full-stack Python/SQL developer at Wissen Infotech. What I work with: Python · R · LangGraph · LangChain · LlamaIndex · Azure OpenAI · FAISS · RAG · BM25 · Vector Search · FastAPI · Snowflake · Vertex AI · PySpark · Kafka · Docker · Kubernetes · MLflow · Airflow · Terraform · PyTorch · TensorFlow · SQL · Power BI Open to full-time roles in AI/ML Engineering, GenAI, MLOps, and Data Engineering. 📩 Panjugaa@mail.uc.edu ## Work Experience ### AI Scientist - RAG & LLM (R&D - MBIC) @ Procter & Gamble Jan 2024 | Ohio, United States Architected and shipped StatVisor, a production-grade multi-agent AI system using LangGraph, LangChain, LlamaIndex, Azure OpenAI GPT-4, and FAISS-backed RAG pipelines — engineered prompt engineering, function/tool calling, context grounding, BM25 sparse + dense hybrid retrieval, and OCR-based document ingestion pipelines for context-aware extraction across large enterprise datasets • Built end-to-end LLM governance and observability infrastructure using FastAPI, Azure Monitor, and MCP — enforced audit logging, cost controls, schema validation, and responsible AI compliance across production AI systems • Built and evaluated advanced ML models using XGBoost and scikit-learn — produced performance dashboards using Matplotlib and Seaborn for senior stakeholder reporting and data-driven decision making • Engineered cloud data pipelines on Snowflake — queried and transformed data using Snowflake Cortex LLM functions, vector search, and Cortex Analyst for AI-native retrieval workflows; built and maintained cross-platform pipelines across BigQuery and Redshift • Orchestrated multi-step agentic workflows using LangGraph, LangChain, LlamaIndex, and Semantic Kernel with guardrails, multi-hop reasoning, and tool-calling agents; implemented data governance and metadata management frameworks aligned with enterprise cataloging standards (Collibra); built graph-based retrieval pipelines using Neo4j and Stardog for relationship-aware knowledge graph use cases • Built multiple production R tools and statistical REST APIs using R, Plumber, ggplot2, dplyr, and tidyverse; engineered async Python (FastAPI) backend services with complex SQL across Snowflake, BigQuery, and Redshift; presented AI and statistical solutions directly to senior P&G leadership, translating complex system outputs into governance-aligned business decisions ### Software Engineer - AI automation and Supply Chain Workflows @ Analytics Quad4 Jan 2023 – Jan 2024 | Bengaluru • Built and deployed AI/ML models and generative AI agents in Python and R automating supply chain insights and actions — implemented hallucination mitigation strategies including output validation, grounding, and confidence scoring; developed production-grade pipelines with feature engineering and model versioning across planning, logistics, and inventory optimization workflows • Architected scalable data and analytics solutions on Google Cloud using Vertex AI — trained and deployed ML models into enterprise supply chain platforms; designed high-throughput Python data pipelines with concurrency and load-balancing algorithms reducing processing overhead by 35% across millions of supply chain records • Built automated multi-threaded scraping (Selenium) and scoring system processing 11K+ sources in under 3 seconds with 95%+ accuracy — applied heuristics and optimization techniques for warehouse resource utilization, lane allocation, and AI-driven scheduling; adopted as a framework standard by 15+ engineers across 2 teams • Developed Power BI dashboards with DAX and semantic layer modeling for supply chain KPI tracking; orchestrated end-to-end ML workflows using Vertex AI, Airflow, MLflow, Docker, Kubernetes, Terraform, Spark, and Kafka for scalable, production-ready data infrastructure ### Software Engineer Intern (MLOps) @ Wissen Infotech Jan 2022 – Jan 2022 | huderabad, india • Built and optimized large-scale data pipelines using PySpark, Delta Lake, and Python — performed data analysis, validation, and SQL operations on Delta tables; tracked experiments and managed model lifecycle using MLflow ensuring quality and reliability across high-volume datasets • Engineered and orchestrated end-to-end data workflows on Azure Cloud using ADF and Airflow — leveraged Azure compute and storage services with Docker-containerized services for scalable, production-grade data engineering and MLOps workloads • Collaborated with cross-functional teams to design and deliver data solutions — implemented CI/CD practices for pipeline deployments, monitored workflow observability, and optimized pipeline performance across the full data lifecycle using Azure DevOps and Git ## Education ### Master of Engineering - MEng University of Cincinnati - College of Engineering and Applied Science ### Bachelor of Engineering - BE Mahindra University ## Contact & Social - LinkedIn: https://linkedin.com/in/panjugulaanvita - Portfolio: https://anvitaxreddy.github.io - GitHub: https://github.com/anvitaxreddy - Email: mailto:panjugaa@mail.uc.edu --- Source: https://flows.cv/anvita JSON Resume: https://flows.cv/anvita/resume.json Last updated: 2026-04-17