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Machine Learning Engineer | Specializing in MLOps, Cloud Infrastructure, and AI Deployment
This talking face animation is my own creation, powered by AI technology I implemented.
I'm Gissella Gonzalez — professionally known as Gichelli Vento — a Machine Learning and MLOps Engineer with a strong foundation in DevOps and Cloud Engineering. With over 6 years of experience across public and private sectors, I specialize in building intelligent, production-grade systems using cutting-edge technologies like LLMs, RAG pipelines, and real-time telemetry. My career spans work in high-impact environments including the Consumer Financial Protection Bureau and innovative startups, where I’ve designed scalable ML pipelines with AWS SageMaker, Databricks, and Kubernetes, and deployed AI solutions that automate workflows, accelerate experimentation, and unlock insights from complex data.
Whether leading MLOps architecture in cloud-native stacks, integrating NLP into customer systems, or forecasting system performance with deep learning models, I bring a rare blend of hands-on engineering and strategic foresight. I'm passionate about bridging AI research and real-world application — and I build with the future in mind.
Production-grade data pipelines built with SageMaker, Vertex AI, and Azure ML Studio.
Built Retrieval-Augmented Generation systems using Hugging Face and OpenAI APIs.
Deployed BERT and CV models for document classification, chat feedback, and real-time analysis.
Orchestrated workflows using MLflow, Kubeflow, Azure DevOps, and AKS.
Designed scalable storage & analytics systems with Databricks and Azure Data Lake.
Built deep learning models for predictive maintenance and system monitoring.
Custom ML models, NLP solutions, and AI consulting to transform your data into actionable insights.
End-to-end pipeline design, model deployment, monitoring, and automated retraining workflows.
CI/CD pipeline implementation, infrastructure as code with Terraform, automated testing, Kubernetes deployments, and Azure DevOps integrations.
Built a RAG-based Q&A system using LangChain, GPT-4, and ChromaDB to answer questions from large document sets.
FastAPI system for real-time predictions and scheduled training with PostgreSQL backend.
Automated ML lifecycle using Azure DevOps, MLflow, and AKS for continuous deployment.