Sean Slavich
AI/Machine Learning Engineer @ Amazon Web Services (AWS)
- Role
- Ai Machine Learning Engineer at Amazon Web Services (AWS)
- Location
- Santa Monica, CA, US
- LinkedIn followers
- 500 followers
About Sean Slavich
I build end-to-end AI/ML workflows as a Data and ML engineer at AWS. I graduated…
Experience
Ai Machine Learning Engineer
Sep 2023 — Present · Santa Monica, CA, US
Architected and migrated prompt engineering solution from Claude 3 to Nova platform for standardizing Amazon promotion narratives, improving consistency and reducing errors while adhering to writing guidelines- Built end-to-end instance segmentation pipeline using PyTorch MaskRCNN and AWS SageMaker, reducing safety incidents by 25% across global warehouses and optimizing costs via optimized serverless architecture ($735/month)- Engineered a serverless facial anonymization system via OpenCV, AWS Rekognition, and AWS Lambda, processing millions of images a day while maintaining high accuracy for PPE detection- Led cross-functional teams to manage 70+ annotation jobs for thousands of images across 20+ warehouse camera angles via AWS SageMaker GroundTruth- Spearheaded the development of an hourly golden dataset pipeline for a Fortune 100 company that cleaned 70GB of raw clickstream data per day and tracked 9 new features and 9 online metrics using AWS EMR and PySpark, significantly improving search tracking- Deployed production action recommendation model for Fortune 100 company using AWS Personalize with automated weekly retraining, serving thousands of users and improving their search experience- Created evaluation pipeline using multiple LLMs for consensus-based relevance assessment of search results- Deployed multiple AI trip planner agents for Fortune 500 company using AWS Bedrock Agents framework, integrating with company APIs via Lambda- Implemented observability tracing for LlamaIndex agents via OpenTelemetry and AgentCore Observability- Built full-stack AI application with React frontend that automates Solutions Architecture design document generation through structured workflow processing of customer call transcripts, leveraging AWS MCP servers and Strands agent hosted on AgentCore Runtime.
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