Related skills
docker aws grafana prometheus python📋 Description
- Design, build, and maintain scalable ML infrastructure on AWS using SageMaker.
- Collaborate with product teams to develop MVPs for AI features with fast iterations.
- Develop monitoring and alerting frameworks for ML models to ensure reliability.
- Enable departments to leverage AI/ML models, incl. Generative AI for various use cases.
- Provide production support; debug ML model issues and participate in on-call rotations.
- Design and scale ML architecture to support rapid growth using AWS and best practices.
- Mentor team members; conduct code reviews and raise overall capabilities.
- Stay updated with ML and AWS advances; drive adoption of cutting-edge solutions.
🎯 Requirements
- Bachelor's degree in CS/CE/ML/Stats/Physics or equivalent.
- 6+ years in ML engineering with production deployments.
- Deep expertise in ML/recommender systems/AI.
- Proven track record delivering ML models from inception to impact.
- Python required; Go (Golang) experience a plus.
- Kubernetes, Docker, and CI/CD for ML deployment.
🎁 Benefits
- Remote-friendly culture.
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