Senior Machine Learning Operations Engineer
Tasks
- Automate training validation deployment and retraining workflows
- Build and maintain end-to-end MLOps pipelines
- Build infrastructure with infrastructure as code
- Deploy monitor and optimize machine learning models in production
- Develop data and machine learning pipelines
- Implement CI CD for machine learning workflows
- Integrate machine learning systems into production applications
- Manage model governance versioning and lineage
- Monitor model performance detect drift and trigger retraining
- Serve models via REST APIs
Perks/Benefits
- Access to modern cloud infrastructure and engineering tooling
- Collaborative high impact environment
- Competitive compensation package
- Exposure to advanced generative AI and MLOps practices
- Flexible remote friendly working environment
- Health and wellness benefits
- Learning culture
Skills/Tech-stack
Agent systems | Amazon Web Services | Auditing | CI/CD | Cloud platform | Deep learning | Distributed Computing | Distributed Systems | Docker | Drift Detection | Google Cloud | Google Cloud Platform | Hugging Face | Hugging Face Transformers | Infrastructure as Code | Language Models | Large Language Models | Lineage Tracking | MLOps | Machine Learning | Microsoft Azure | Model Evaluation | Model Governance | Model versioning | Monitoring | Prompt engineering | PyTorch | Python | REST APIs | Scala | Scikit-learn | Serverless | Supervised Learning | TensorFlow | Terraform | Unsupervised Learning | Web Services | “as-code”
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