AI-Powered Resume Portfolio

Traditional resumes fail to convey nuanced fit information. Employers waste time on candidates who look good on paper but aren't right for the role. Needed a system that could honestly assess fit and provide contextual responses about experience.

Built an AI-powered portfolio with GPT-4 integration featuring: (1) RAG pipeline using pgvector for semantic search across resume data, (2) Carefully engineered prompts that maintain honesty while being helpful, (3) Job description analyzer that provides fit scores with honest gap analysis, (4) Redis-backed session management for conversation continuity, (5) Streaming responses for real-time chat experience.

Backend: FastAPI with async SQLAlchemy, OpenAI GPT-4 API with streaming, pgvector for vector embeddings and semantic search, Redis for session/rate limiting, Alembic migrations. Frontend: Next.js 14 with TypeScript, Tailwind CSS, shadcn/ui components. Infrastructure: Docker Swarm, Cloudflare Tunnel, PostgreSQL. LLMOps: Circuit breaker pattern for API resilience, token cost tracking, prompt injection protection.

• →Implemented RAG (Retrieval-Augmented Generation) pipeline using pgvector for semantic search, enabling contextually relevant responses about experience and skills
• →Implemented RAG (Retrieval-Augmented Generation) pipeline using pgvector for semantic search, enabling contextually relevant responses about experience and skills
• →Designed and iterated on prompt templates for GPT-4 that maintain brutal honesty while being helpful, including handling edge cases and preventing prompt injection
• →Designed and iterated on prompt templates for GPT-4 that maintain brutal honesty while being helpful, including handling edge cases and preventing prompt injection
• →Built agentic workflow for job description analysis that extracts requirements, matches against profile data, and generates honest fit assessments with specific gap identification
• →Built agentic workflow for job description analysis that extracts requirements, matches against profile data, and generates honest fit assessments with specific gap identification
• →Implemented circuit breaker pattern with tenacity for OpenAI API resilience, graceful degradation, and fallback responses when AI is unavailable
• →Implemented circuit breaker pattern with tenacity for OpenAI API resilience, graceful degradation, and fallback responses when AI is unavailable
• →Created streaming chat interface with real-time token generation, Redis session management, and conversation history for multi-turn interactions
• →Created streaming chat interface with real-time token generation, Redis session management, and conversation history for multi-turn interactions
• →Developed JSON-based knowledge entry system for structured data that feeds into LLM context, with configurable priority and inclusion rules
• →Developed JSON-based knowledge entry system for structured data that feeds into LLM context, with configurable priority and inclusion rules

Demonstrates practical LLM application development including RAG implementation, prompt engineering for reliable outputs, and production-grade error handling. The system provides honest assessments that have helped employers quickly determine fit.