NXP MLOps Engineering
@ NXP
Designed AWS foundations for production ML workloads and delivered a Bedrock-based assistant for technical knowledge access.
Technologies
Key Highlights
- Built production ML pipelines using AWS CDK and SageMaker
- Deployed AI Chatbot to production using Ansible, CDK, and Bedrock
- Led development of project management app for secure IP access
- Trained multiple teams on CI/CD best practices
The Challenge
When joining NXP’s Hardware Design Analytics team, data scientists were great at creating statistical and ML models, but deployment automation was something they could only dream about. The team needed:
- Simple, reproducible development environments
- Dependable CI/CD pipelines
- Production-ready deployments
The Solution
I brought DevOps excellence to the ML workflow:
ML Pipeline Infrastructure
Built infrastructure for training and deploying ML models using AWS CDK and SageMaker. The models support predictive analytics used in hardware-design workflows.
AI Chatbot Deployment
Brought an AI-powered assistant to production using Ansible, AWS CDK, and Amazon Bedrock, giving designers a production interface for accessing technical information.
Project Management Application
Led development of a Django-based application running on ECS, enabling secure access to intellectual property across teams.
CI/CD Training & Support
Helped multiple teams level up their ways of working through hands-on training and support in GitLab CI/CD processes.
Technologies Used
- Cloud: AWS (CDK, SageMaker, Bedrock, ECS)
- Backend: Python, Django
- Infrastructure: Ansible
- CI/CD: GitLab CI