Director, Fleet AI Engineering
Tasks
- Balance latency reliability cost
- Build AI assisted development workflow
- Build data pipelines and model training serving infrastructure
- Define AI ML engineering roadmap for fleet use cases
- Define acceptance criteria and run experiments
- Design and scale production AI systems
- Develop simulation environments for decisioning
- Establish prompting and code review standards
- Implement automated testing and documentation
- Implement observability and drift monitoring
- Mentor a small team player coach model
- Own end to end ML system lifecycle
- Perform offline evaluation and production deployment
- Run controlled rollouts and iterate on real world performance
- Train and evaluate sequential decision making methods
- Translate business goals into measurable ML objectives
Perks/Benefits
- Educational reimbursement
- Employee assistance program
- Gym discounts
- Medical/Dental/Vision
- Paid parental leave
- Paid time off
- Retirement 401(k) matching
- Theme park tickets
- Voluntary insurance programs
Skills/Tech-stack
AWS SageMaker | Azure ML | CI/CD | Cloud platform | Data Pipelines | Decision Making | Distributed Systems | Drift monitoring | Experiment tracking | Experimentation | Feature Engineering | Fine Tuning | Google Cloud | Google Cloud Platform | Gymnasium | LLM Fine-tuning | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Machine Learning | Model versioning | Monitoring | Observability | PettingZoo | Prompt engineering | PyTorch | Python | Ray RLlib | Reinforcement Learning | Scikit-learn | Sequential decision making | Stable-Baselines3 | TensorFlow
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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