Applied AI Engineer
Tasks
- Add security guardrails and audit trails
- Build Generative AI features
- Build MLOps pipelines for training deployment monitoring
- Create AI demos for customers and stakeholders
- Deploy AI features in production
- Design multi-agent systems
- Develop LLM copilots and autonomous agents
- Develop predictive models for business problems
- Evaluate and improve model performance and user impact
- Identify AI use cases for accounts receivable
- Implement human-in-the-loop workflows
- Integrate AI and LLM solutions via REST APIs
Perks/Benefits
- Company equity
- Comprehensive benefits
- Counseling
- Gym subsidies
- Hybrid work options
- Mentorship opportunities
- Paid time off
- Parental leave
- Volunteer days
- Well-being programs
Skills/Tech-stack
AWS | Agent systems | Authentication | Autonomous Agents | Azure | CI/CD | Cloud platform | Generative AI | Git | GitHub | GitLab | Google Cloud | Google Cloud Platform | HTTP | Human-in-the-loop | JSON | Java | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Deployment | Model Monitoring | Multi-Agent | Multi-Agent Systems | NLP | Natural Language | Natural Language Processing | Orchestration | Predictive Modeling | PyTorch | Python | R | REST API | Routing | Scikit-learn | Secure Software | Secure Software Development | Software development | TensorFlow | The Loop | Version control
Education
N/A
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