user1826272

Architected a modular AI backend using Python, FastAPI, and Node.js to isolate LLM inference from synchronous business APIs through orchestration services, bounded execution, and standardized contracts. Engineered hybrid RAG combining dense embeddings with BM25 keyword matching, metadata filtering, and context deduplication to improve retrieval precision within LLM context limits. Designed resilient LangGraph agent workflows with explicit state transitions, checkpointing, conditional routing, and idempotent operations to recover from failures without duplicating side effects. Built incremental document ingestion pipelines using fingerprinting, configurable chunking, metadata versioning, and selective embedding updates to avoid full reindexing. Developed asynchronous inference services with concurrency limits, timeouts, cancellation handling, and streamed responses so clients can display partial results. Hardened tool integrations using schema-validated inputs, execution boundaries, ret

Built React, Next.js, and GraphQL features to reduce excessive data fetching and eliminate N+1 patterns using reusable component boundaries, selective GraphQL queries, and batched resolvers. Optimized Next.js delivery by separating server-rendered content from client interactions, applying route-level code splitting, and minimizing hydration work. Decomposed backend capabilities into microservices using NestJS, Flask, and FastAPI with versioned API contracts, request validation, and explicit failure handling. Implemented RabbitMQ-based asynchronous workflows with durable messaging, dead-letter queues, bounded redelivery, and idempotent consumers to prevent duplicate side effects. Developed Python NLP and ML pipelines for classification and information extraction, addressing training-serving inconsistencies. Implemented vector-based semantic retrieval with relevance thresholds, metadata filtering, and embedding similarity to improve contextual content discovery. Prototyped LLM-powered w

Developed data-intensive Vue.js and React Native interfaces backed by GraphQL APIs, improving responsiveness via selective field retrieval, reusable components, and incremental data loading. Engineered Python and Django backend services with GraphQL interfaces, resolving inefficient nested data access using batched queries, indexed lookups, and pagination. Built distributed processing workflows using Apache Kafka with partition-aware processing, controlled offset commits, and idempotent handlers to improve recoverability. Optimized high-frequency requests using Redis caching with bounded expiration and targeted invalidation. Developed ML pipelines with TensorFlow, PyTorch, and scikit-learn for feature preparation, training, evaluation, and inference, improving reproducibility. Automated AWS infrastructure provisioning using Terraform to reduce environment drift and support repeatable deployments.