Gabe

About Gabe

I’m deeply passionate about Generative AI and building intelligent, autonomous systems that can reason, adapt, and solve real-world problems.🧠 Currently exploring the cutting edge of Agentic AI, LLM frameworks, and multi-agent collaboration with tools like LangChain, CrewAI, LangGraph, and OpenAI SDK. Enrolled in the Agent Engineering Bootcamp I’m committed to mastering agent workflows, RAG pipelines, and AI product development.With 5+ years of experience in data-driven problem solving—building analytics solutions —I bring strong foundations in Python, SQL, ML, and system design autonomous AI agents.Domain Interests: Machine Learning | Generative AI | FinTechSkill Highlights :* Complete ML/AI model development life cycle* Data Pipelining (Dagster.io | Lambda | Cloud Function)* Machine Learning/AI as a service* Web Applications (Python | Typescript | Javascript | Ruby on Rails | REST | GraphQL | TailwindUI)* Deployment (CI/CD | DevOps | MLOps | LLMOps)* Cloud Platforms ( AWS | GCP )Large Language Models:* Proprietary LLM Platforms: OpenAI, Anthropic Claude, AWS Bedrock* Open Source LLM Platform: Hugging face* LLM Orchestration Frameworks: LangChain, LangGraph* Vector Databases: Weaviate, PineconeKey Competencies:Probability || Statistics || Machine Learning || Deep Learning || Natural Language Processing (NLP)|| Forecasting || Generative AI (LLM, Fine-tuning, RAG)Tech Stack :Python || Langchain || LangGraph || PyTorch || Keras || SQL & No-SQL Database || AWS || GCP

Experience

1. Ai Ml Engineer Data Scientist

   Constellis

   Dec 2019 — Present

   Designed and implemented advanced deep learning models using TensorFlow and PyTorch, focusing on solving complex tasks in computer vision and natural language processing.• Developed and optimized Convolutional Neural Networks (CNN) for image classification and object detection, achieving state-of-the-art performance on benchmark datasets.• Utilized Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for sequence prediction and natural language processing tasks, improving accuracy and efficiency.• Created and trained Generative Adversarial Networks (GANs) to generate realistic images and improve data augmentation strategies for training robust models.• Conducted experiments with Neural Architecture Search (NAS) to optimize model architectures and enhance performance while reducing training time.• Employed Transfer Learning to adapt pre-trained models for specific applications, improving training efficiency and accuracy.• Developed end-to-end deep learning pipelines for data preprocessing, model training, and deployment, ensuring efficient and scalable solutions.• Employed Hugging Face Transformers library for state-of-the-art NLP models, fine-tuning pre-trained models for specific use cases and improving inference speed.• Conducted extensive model evaluation and hyperparameter tuning using tools like GridSearchCV and RandomizedSearchCV to ensure optimal performance.• Monitored and analyzed model performance, employing techniques like AutoML for automated model selection and hyperparameter optimization.• Collaborated with cross-functional teams to identify business needs and translate them into technical specifications for deep learning solutions.• Stayed informed about advancements in deep learning, integrating relevant innovations into projects to maintain a competitive edge in the field.