Art Turner

AI Application Engineer · GenAI / RAG · Python · AWS

Data-driven by default. Relentless by nature.

• Design and deploy RAG pipelines, LLM orchestration, and multi-agent workflows end-to-end.
• Strong Python foundation with a production mindset: containers, APIs, evaluation, CI/CD.
• AWS-certified across 7 credentials including GenAI Developer (Professional) and ML Specialty.

Projects

Selected work (problem → approach → result).

RAG-powered decision-support application with conditional routing, grounding verification, and provider-agnostic LLM integration.

Multi-node RAG pipeline in LangGraph with conditional routing across five query types and configurable retrieval strategies
Hallucination mitigation via token-overlap grounding verification, retry/refuse loop, and dual confidence thresholds
Evaluation framework with groundedness, relevance, and refusal-correctness scorers

PythonLangGraphFastAPIFAISSChromaDBOpenAIAnthropic ClaudeAWS BedrockOllama

Agent-based research pipeline with parallel tool execution, self-critique revision, and Pydantic-validated inter-agent contracts.

4-agent pipeline (TopicSplitter, parallel Researchers, Synthesizer, Critic) with concurrent execution
Self-critique revision loop evaluating factual consistency, citation accuracy, and logical coherence
Pydantic-validated inter-agent data contracts for reliable structured data flow

PythonOpenAITavilyPydanticThreadPoolExecutorGradio

Full-stack GenAI application with event sourcing, deterministic replay, scenario versioning, and admin analytics.

Containerized full-stack app (Next.js, FastAPI, PostgreSQL, Docker Compose) with OpenAPI-documented APIs
Event-sourced session model with custom lexer/parser/AST evaluator eliminating code injection risk
3,200+ line test suite and Playwright E2E coverage

PythonTypeScriptNext.jsFastAPIPostgreSQLDockerPlaywrightPydantic

Multi-format document ingestion pipeline with TF-IDF content alignment and provider-agnostic LLM orchestration.

6-stage pipeline ingesting unstructured documents (PowerPoint, PDF, RTF, DOCX) with style-aware heading detection
TF-IDF cosine similarity for cross-document content alignment
Provider-agnostic LLM layer (OpenAI, Gemini, Grok) with priority-based fallback for graceful degradation

PythonNLPTF-IDFscikit-learnOpenAIGoogle GeminiPydantic

Production-grade AI pipeline with SHA256-keyed caching, rate limiting, checkpoint/resume, and multi-format export.

4-stage ETL pipeline with YAML-driven config, SHA256-keyed disk caching, RPM/TPM rate limiting, and exponential backoff retry
Checkpoint/resume system eliminating redundant API calls and enabling mid-run recovery
Multi-format export adapters (structured data API, mobile JSON API, Next.js PWA) with Pydantic v2 cross-field validators

PythonTypeScriptNext.jsClaude APIPydantic v2VercelYAML

LangGraph, LangChain, RAG pipelines, embeddings, vector search, prompt engineering, hallucination mitigation, multi-agent orchestration

Python, TypeScript, SQL, FastAPI, Next.js, Pydantic v2, PyTorch, scikit-learn

AWS (7 certs), Docker, CI/CD, REST APIs, OpenAPI, structured logging, monitoring