About the Role
Design and build autonomous AI agents capable of performing complex tasks and decision-making in dynamic environments.
Responsibilities
- Develop core logic for AI-driven agent behaviors
- Implement machine learning models into agent frameworks
- Optimize agent performance under real-world constraints
- Collaborate with research teams to integrate new algorithms
- Design agent communication and coordination protocols
- Test agent responses in simulated environments
- Debug and refine agent decision pathways
- Ensure scalability of agent systems across use cases
- Maintain documentation for agent architectures
- Improve agent learning efficiency over time
- Support deployment of agents in production settings
- Evaluate agent safety and reliability metrics
- Refactor code for improved maintainability
- Integrate feedback loops for adaptive behavior
- Work with product teams to define agent capabilities
- Monitor agent performance post-deployment
- Contribute to ethical AI guidelines for agent design
- Troubleshoot system-level agent interactions
- Assist in creating training environments for agents
- Stay current with advancements in AI agent research
- Write unit and integration tests for agent modules
- Participate in code reviews and technical planning
- Improve data pipelines used by learning agents
- Design fallback strategies for agent failures
- Collaborate on user-agent interaction models
Compensation
Competitive salary based on experience
Work Arrangement
Remote with flexible hours
Team
Small, cross-functional team focused on rapid development
Tech Stack
Python, PyTorch, Docker, AWS, Git, Redis, Kubernetes, Gunicorn, PostgreSQL, LangChain
What We Value
- Technical depth over formal credentials
- Ownership of projects from concept to deployment
- Clear communication in asynchronous settings
- Curiosity about agent-based AI frontiers
- Focus on real-world agent reliability
Available for qualified candidates
