Ranadeep Prathapagiri

About Ranadeep Prathapagiri

Senior Python Full-Stack Engineer specializing in building enterprise AI/ML and GenAI platforms—from secure backend services to user-facing dashboards—across Banking, Healthcare, and Networking. I focus on turning complex business workflows into production systems that are scalable, explainable, and compliant, especially in regulated environments.In recent roles, I’ve led delivery of LLM/RAG-based applications for customer servicing automation, document intelligence, internal knowledge search, and decision support. I design end-to-end architectures that combine structured data + unstructured documents, integrating vector search and multi-step agent workflows (LangChain/LangGraph) with strong controls around security, auditability, and governance.What I do bestPython backend & microservices: FastAPI / Flask / Django, async processing, API design, integrationsAI/ML + GenAI: RAG pipelines, embeddings, semantic search, summarization, entity extraction, classificationLLM engineering: OpenAI / Azure OpenAI / HuggingFace + open-source models; prompt templates, versioning, token/cost optimizationMLOps & production readiness: MLflow, CI/CD, monitoring, drift detection, rollback strategiesCloud-native delivery: Docker, Kubernetes, AWS/Azure, scalable inference for batch + real-time workloadsFull stack delivery: React / Angular dashboards for AI-driven workflowsDomain experienceBanking: customer inquiry automation, loan document analysis, internal knowledge search, compliance-oriented AI servicesHealthcare: clinical note summarization, document processing, patient interaction workflows; HIPAA-aligned securityNetworking: ML-based anomaly detection and telemetry analytics for proactive monitoringI enjoy ownership-heavy roles—architecture, delivery, code reviews, mentoring—and partnering with product, compliance, and business teams to ship systems that are both useful and production-safe.Tech highlights: Python, FastAPI, Flask, Django, React, TypeScript, LangChain, LangGraph, RAG, Pinecone/FAISS/Chroma/Azure Cognitive Search, MLflow, PostgreSQL/MongoDB, Redis, Kafka, Docker, Kubernetes, AWS, Azure.

Experience

1. Machine Learning Python Full Stack Engineer

   Webster Bank

   Jan 2025 — Present · Stamford, CT, US

   Designed and delivered LLM-powered applications for customer inquiry automation, loan document analysis, fraud signal enrichment, and internal knowledge search using RAG.Built Retrieval-Augmented Generation pipelines integrating vector DB retrieval with enterprise sources (core banking/CRM, PDFs, regulatory content) to improve accuracy and auditability.Implemented agentic workflows using LangChain/LangGraph for document classification, entity extraction, risk scoring, and compliance validation.Integrated OpenAI/Azure OpenAI and open-source LLMs (LLaMA, Mistral) with prompt templates, versioning, and token/cost optimization.Developed secure FastAPI/Flask microservices exposing AI/ML capabilities with role-based access and API gateway controls.Productionized inference with Docker/Kubernetes for real-time + batch workloads; applied async processing, caching, and batch inference for performance.Established MLOps pipelines using MLflow + CI/CD for model validation, versioning, deployments, monitoring, and rollback strategies.Delivered low-latency semantic retrieval using Pinecone/FAISS/Azure Cognitive Search across large document and metadata sets.Enforced data security controls (PII masking, encryption, access logging) aligned with SOC2/GDPR and banking governance requirements.Led code reviews and guided junior engineers; participated in client demos, architecture reviews, and production readiness assessments.Skills/Tech: Python, FastAPI, Flask, React, TypeScript, LangChain, LangGraph, RAG, OpenAI/Azure OpenAI, LLaMA, Mistral, Pinecone, FAISS, Azure Cognitive Search, MLflow, Docker, Kubernetes, Kafka, PostgreSQL, MongoDB, Redis, GitHub Actions/Azure DevOps, AWS, Azure, Terraform, Linux

Education

• University at Buffalo

  Master's degree

  2023