Lambda
Experienced
Hello, I'm
Sergio Rayon Mora
Senior AI Engineer
Hello, I'm
Senior AI Engineer
Get To Know More
8+ years
AI and Data Development
I'm a passionate and experienced AI/ML Engineer with a strong foundation in cloud architecture, machine learning operations, and data engineering. I hold multiple AWS certifications (AI Practitioner, Developer Associate, Solutions Architect Associate), a Databricks Machine Learning Associate certification, and an Oracle DBA certification, complemented by a Master's degree in Data Science from the University of Bath and a B.Eng. in Computer Systems Engineering.
With hands-on experience building production grade AI systems, I've developed end-to-end ML solutions including semantic search engines, time series forecasting models, and NLP-powered fraud detection systems. I specialize in designing and deploying generative AI applications on AWS, implementing RAG systems with knowledge bases, and building agentic workflows using AWS Bedrock Agents.
My expertise spans the full ML lifecycle from data pipeline development and model training to deployment and monitoring. I've built scalable cloud-native architectures using Lambda, API Gateway, and event-driven patterns, while ensuring reliability through MLOps best practices including CI/CD pipelines and Infrastructure as Code (IaC) with AWS SAM and CloudFormation.
I've also delivered business value through BI solutions, creating interactive dashboards with AWS QuickSight, Elasticsearch, and Kibana that drive data-informed decision-making. Whether it's architecting AI solutions on Databricks, optimizing data pipelines with Apache Airflow, or deploying models via FastAPI and SageMaker.
Currently enhancing my cloud expertise through intensive Digital Cloud Training as an AWS AI/ML Engineer, I'm committed to staying at the forefront of AI innovation and cloud technologies. I'm seeking opportunities to contribute to impactful projects where I can apply my unique blend of AI engineering, cloud architecture, and data science expertise.
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Browse My Recent
A cost-efficient, fully serverless RAG (Retrieval-Augmented Generation) implementation leveraging AWS's newly launched S3 Vector Buckets. This project demonstrates how to build production-ready conversational AI that can interact with documents for under a dollar, achieving up to 90% cost savings compared to traditional vector database solutions.
Key Features:The architecture orchestrates document ingestion through Lambda functions that parse and chunk PDFs, generates embeddings via Amazon Bedrock, stores vectors in S3 Vector Buckets, and maintains conversation memory using DynamoDB.
Technologies:AWS Lambda, Amazon Bedrock, S3 Vector Buckets, DynamoDB, SAM, Python.
An autonomous multi-agent system that transforms any topic into an edited breaking news video with web research, multilingual voice-overs, and sentiment-aware editing, all generated in under 3 minutes. Built as an exploration of AWS Strands Agents SDK for orchestrating complex AI workflows.
Core Capabilities:The system employs a hierarchical agent structure using AWS Strands Agents SDK:
Built on AWS Strands Agents SDK (AWS's open-source agentic framework), the system leverages Claude via Amazon Bedrock for agent reasoning, custom tool development for specialized tasks, and serverless deployment via Lambda, API Gateway and S3.
Technologies:AWS Strands Agents SDK, Amazon Bedrock (Claude), Tavily API, Amazon Polly, Google Veo 3, AWS Lambda, S3, SAM.
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