About the Role
The candidate will lead the development of AI-driven features within a full-stack environment, bridging backend systems with intelligent frontend interfaces while maintaining system reliability and performance.
Responsibilities
- Design and implement scalable backend services supporting AI functionality
- Develop responsive user interfaces that integrate machine learning outputs
- Collaborate with data scientists to operationalize AI models
- Write clean, maintainable code across frontend and backend tiers
- Optimize application performance and user experience
- Integrate third-party APIs and data sources
- Maintain robust security practices in AI-enabled systems
- Troubleshoot and debug production issues
- Participate in code reviews and technical planning
- Ensure compliance with data privacy standards
- Deploy and monitor AI-powered applications
- Manage version control and CI/CD pipelines
- Document system architecture and workflows
- Support testing frameworks for AI components
- Refactor legacy systems to support AI integration
- Implement real-time data processing pipelines
- Work with containerized environments and orchestration tools
- Monitor system health and usage metrics
- Contribute to technical design discussions
- Align development with product roadmap goals
Nice to Have
- Master’s degree in computer science or AI-related field
- Experience with large-scale AI system deployment
- Contributions to open-source AI projects
- Prior work in regulated industries
- Familiarity with MLOps tools
- Experience with real-time AI inference systems
- Knowledge of edge computing for AI
- Background in ethical AI development
- Leadership in technical project delivery
Compensation
Competitive salary with performance-based incentives
Work Arrangement
Hybrid remote with core collaboration hours
Team
Cross-functional team integrating AI and software development
Technology Stack
- Frontend: React with TypeScript
- Backend: Node.js and Python microservices
- AI Frameworks: TensorFlow, Hugging Face Transformers
- Infrastructure: AWS, Docker, Kubernetes
- Databases: PostgreSQL, MongoDB
Development Practices
- Daily stand-ups and sprint planning
- Code reviews using GitHub
- Automated testing and deployment
- Monitoring via Datadog and Sentry
- Documentation in Confluence
Available for qualified candidates
