Lead AI/ML Engineer
Tasks
- Build and orchestrate machine learning pipelines
- Collaborate with stakeholders to align AI roadmaps
- Define AI ML technical vision
- Deploy and scale AI/ML models
- Develop scalable AI/ML pipelines
- Establish model monitoring logging and versioning
- Implement MLOps practices
- Integrate AI ML platforms with cloud environments
- Integrate AI capabilities into enterprise applications
- Lead AI/ML architecture
- Monitor AI ML research for innovation
- Optimize AI ML for latency scalability reliability performance cost
- Transform prototypes into production systems
Perks/Benefits
- Background check may be required
- Equipment provided
- Flexible work environment
- Paid sick leave
- Paid vacation
- Professional development
- Remote work
- Training allowance
- Volunteer paid time off
- Wellness budget
- Work-life balance
Skills/Tech-stack
AWS | Agentic AI | CI/CD | Cloud platform | Deep learning | Docker | ELT | ETL | Generative AI | Git | Google Cloud | Google Cloud Platform | Kubeflow | Kubernetes | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Microsoft Azure | Model Explainability | Model Monitoring | Model versioning | Observability | PyTorch | Python | Scikit-learn | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI/ML Engineer | Engineer | Lead | Lead AI | Lead AI/ML Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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