Lead Machine Learning Engineer
Tasks
- Architect production grade ML solutions on GCP
- Build internal accelerators and reusable frameworks
- Define ML practice technical standards
- Develop REST APIs for ML services
- Drive technical strategy for clients
- Ensure performance, scalability, and security
- Implement MLOps pipelines with Infrastructure as Code
- Lead technical discussions and solution presentations
- Mentor and coach engineers
- Optimize and deploy ML models
Perks/Benefits
- Company off-sites and meetups
- Family assistance program
- Group insurance
- Home office budget
- Lifestyle days
- Paid certifications
- Paid vacation
- Public holidays
- Retirement savings plan match
- Udemy Business
- Virtual medicine
- Wellness days
Skills/Tech-stack
AI Pipelines | CI/CD | Cloud platform | Distributed Computing | FastAPI | Flask | Google Cloud | Google Cloud Platform | Gradio | Infrastructure as Code | Kubeflow | MLOps | MLflow | Machine Learning | Python | REST API | SQL | Streamlit | Terraform | Vertex AI | Vertex AI pipelines | “as-code”
Education
N/A
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