I build production systems clients own and run in their own cloud — RAG over operational data, agent workflows, custom apps. The work that pulls me in is the cross-system flows their legacy ERP, BI, and CRM stack couldn't deliver: reporting, approvals, and exports the per-seat tools were never going to build. The licenses retire alongside.

The repos below are the public ones; embedded engagements happen privately.

Practice (outside builds): evanparra.ai

Founding engagement — Regional commercial electrical contractor (NE Florida, anonymized). Embedded in the client's Azure tenant, replacing per-seat ERP workflows app by app — cost-to-complete forecasting, change orders, approvals, and the cross-system flows the per-seat tools were never going to build. ~$150K/yr in retired per-seat ERP cost (and growing). The ERP stays the system of record; the seats don't.

Cross-vertical proof — my own products, both live:

• TextTimeline — legal document intelligence. Messy text exports become attorney-ready chronological timelines with 100% source citations. FAISS + BM25 hybrid retrieval, Cloud Run, Firestore, Gemini. (Source private — paid product.)
• GammaRips — autonomous overnight options-flow scanner. 14 Cloud Run services, ~20 schedulers, multi-agent ADK publishing layer with deterministic compliance gating.

Autonomous trading signal platform processing ~10GB daily market data. Full MLOps lifecycle from ingestion to deployment.

• LLM-augmented ETL with prompt chaining
• MCP server for AI agent tool-calling
• CI/CD: GitHub Actions → Cloud Build → Cloud Run
• 50% inference cost reduction via dynamic model routing

Stack: Python, BigQuery, Vertex AI, Cloud Run, Pub/Sub, MCP

Customer-facing surface for GammaRips. Daily mechanically-held picks, subscription billing, compliance disclosures.

ML core for the GammaRips signal stack. ~3x precision lift versus baseline, with a quarterly retraining cadence.

Model Context Protocol server enabling AI agents to query real-time financial data. Production-deployed on Cloud Run with SSE transport.

Stack: Python, FastMCP, BigQuery, Cloud Run

Production evaluation framework for generative AI systems. NLI-based hallucination detection, factual accuracy verification, content safety scoring, and A/B model benchmarking with statistical significance testing.

• Hallucination detection via cross-encoder NLI + semantic similarity
• Brand safety scoring with configurable content rating (G/PG/PG-13/R)
• A/B comparison engine with paired t-test and effect size analysis
• HTML + JSON reporting for CI/CD integration

Stack: Transformers, Sentence-Transformers, Detoxify, Scikit-Learn, Pydantic

Parameter-efficient fine-tuning of LLMs using QLoRA. 4-bit quantization with PEFT adapters, full training pipeline with experiment tracking.

• QLoRA with BitsAndBytes NF4 quantization
• SFTTrainer from TRL with gradient accumulation
• Weights & Biases experiment tracking and evaluation
• Interactive inference with streaming output

Stack: Transformers, PEFT, TRL, Accelerate, BitsAndBytes, W&B

Text-to-image generation with Stable Diffusion XL, IP-Adapter style conditioning, and content safety guardrails.

• SDXL base + refiner pipeline with safety-first architecture
• Brand consistency scoring via CLIP embeddings
• Content rating system (G/PG/PG-13) for family-friendly generation
• NSFW classification and automated content filtering

Stack: Diffusers, Transformers, OpenCLIP, PyTorch, Pillow

Cross-modal AI pipeline: audio transcription → LLM analysis → structured output. Dual backend support with async orchestration.

• Whisper + Google Cloud Speech-to-Text dual backends
• Gemini-powered analysis: sentiment, entities, topics, action items
• Pydantic-validated structured JSON output
• Async pipeline with retry logic and batch processing

Stack: OpenAI Whisper, Google Generative AI, Pydantic, PyDub

Knowledge-grounded clinical Q&A agent using Graph RAG with Google Cloud. Combines medical knowledge graphs with retrieval-augmented generation for accurate, citation-backed healthcare answers.

Stack: Python, ADK, Gemini, Spanner Graph, Vertex AI, Cloud Run

Multi-agent system automating invoice lifecycle: Ingestion → Validation → Approval → Payment. Self-correction loops for data extraction.

Stack: Python, LangGraph, xAI Grok, FastAPI, Cloud Run

Secure file storage with user isolation and irreversible PII redaction using event-driven architecture.

Stack: Cloud Run, Cloud DLP, Vertex AI, FastAPI

Multi-document scientific paper Q&A with citation tracking. Vertex AI Vector Search + Gemini.

Stack: RAG, Vertex AI, Gemini, FastAPI, Firestore

Computer vision research from M.S. AI coursework at Florida Atlantic University — end-to-end guide for fine-tuning YOLOv9 on custom datasets.

Stack: PyTorch, YOLO, Computer Vision

Generative AI:  Diffusers, PEFT/LoRA, Whisper, Stable Diffusion, CLIP
ML/AI:          Vertex AI, Gemini, TensorFlow, PyTorch, Scikit-Learn
Evaluation:     Sentence-Transformers, Detoxify, W&B, custom frameworks
Cloud:          GCP (BigQuery, Cloud Run, Pub/Sub, Cloud Functions, Vertex AI), Azure (client engagements)
MLOps:          GitHub Actions, Cloud Build, Docker, Model Registry
Data:           Python, SQL, Pandas, dbt, Airflow
Backend:        FastAPI, Python, Node.js
Frontend:       Next.js, React, TypeScript

• M.S. Artificial Intelligence — Florida Atlantic University
• B.A. Economics — Florida International University
• Google Professional Machine Learning Engineer
• Google Advanced Data Analytics
• Lean Six Sigma Green Belt
• EVANPARRA.AI LLC — SAM.gov registered (UEI FPLQTQK39ZE1), SBIR/STTR eligible (CLARA / DARPA proposal submitted Mar 2026)

• Practice: evanparra.ai
• Email: evan@evanparra.ai
• LinkedIn: linkedin.com/in/evanparra

Booking discovery engagements through evanparra.ai. Based in NE Florida; embedded work in Azure or GCP tenants. 5. Update pinned repos (§3) 6. Fix gammarips-engine homepage URL + add lora-finetune-lab topics (§4a-b)