About the Role
Design and deploy machine learning models that power autonomous agents, improve system intelligence, and adapt to real-time data inputs while integrating with existing digital infrastructures.
Responsibilities
- Develop and train machine learning models tailored for autonomous decision-making
- Implement agent-based architectures in production environments
- Optimize model performance under variable operational conditions
- Collaborate with engineering teams to integrate AI components
- Monitor system behavior and refine agent responses
- Debug and resolve issues in agent logic and data pipelines
- Maintain documentation for model development and deployment
- Evaluate emerging ML frameworks for potential adoption
- Ensure compliance with data privacy and security standards
- Contribute to the design of agent interaction protocols
- Support testing and validation of agent behaviors
- Refine training datasets to improve model accuracy
- Work with product teams to define agent capabilities
- Analyze system feedback to guide iterative improvements
- Develop simulation environments for agent testing
- Implement reinforcement learning strategies where applicable
- Ensure models operate efficiently within resource constraints
- Participate in code reviews and technical planning
- Assist in scaling agent systems across multiple platforms
- Stay current with advancements in agentic AI research
Nice to Have
- Master’s degree in artificial intelligence or related field
- Experience with multi-agent systems
- Knowledge of large language model integration
- Background in real-time decision systems
- Familiarity with edge computing for AI
- Experience with model versioning tools
- Publication record in AI or ML venues
- Contributions to open-source AI projects
Compensation
Competitive salary with performance-based incentives
Work Arrangement
Hybrid remote arrangement with core collaboration hours
Team
Cross-functional team focused on autonomous AI systems development
Technology Stack
- Primary languages: Python, JavaScript
- Frameworks: TensorFlow, PyTorch, LangChain
- Infrastructure: AWS, Docker, Kubernetes
- Monitoring: Prometheus, Grafana
- CI/CD: GitHub Actions, Jenkins
Development Culture
- Agile methodology with two-week sprints
- Daily standups and weekly planning
- Emphasis on code quality and testing
- Regular knowledge-sharing sessions
- Open feedback environment
Available for qualified candidates
