I build end-to-end ML systems that ship to production. My focus is autonomous pipelines, generative AI, model evaluation, and LLM-powered applications on Google Cloud Platform.
MS in Artificial Intelligence (FAU, 2025) | Google Certified ML Engineer
| Area | Focus |
|---|---|
| Generative AI | Diffusion models, fine-tuning (LoRA/QLoRA), multi-modal pipelines, content safety |
| ML Pipelines | End-to-end data ingestion → feature engineering → model deployment |
| Evaluation & Safety | Hallucination detection, factual accuracy, brand safety, A/B benchmarking |
| LLM Applications | RAG systems, prompt chaining, MCP tool servers, agent orchestration |
| MLOps | CI/CD for ML, model versioning, monitoring, cost optimization |
| Data Engineering | BigQuery, ETL/ELT pipelines, multi-source integration |
Autonomous trading signal system 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
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
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
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)
MLOps: GitHub Actions, Cloud Build, Docker, Model Registry
Data: Python, SQL, Pandas, dbt, Airflow
Backend: FastAPI, Python, Node.js
Frontend: Next.js, React, TypeScript
- Google Professional Machine Learning Engineer (2025)
- Google Advanced Data Analytics Certificate
- MS Artificial Intelligence — Florida Atlantic University
- LinkedIn: linkedin.com/in/evanparra
- Portfolio: evanparra.ai
- Email: eraphaelparra@gmail.com
- GitHub: You're here
Currently open to ML Engineer, GenAI Engineer, and Data Engineer opportunities. Remote or US-based.