About the Role
This position leads the development and implementation of artificial intelligence solutions integrated within core platform services, ensuring scalability, reliability, and alignment with business objectives.
Responsibilities
- Design and deploy AI-powered integration systems
- Collaborate with product and engineering teams to identify automation opportunities
- Evaluate and integrate third-party AI tools and APIs
- Ensure solutions are scalable, maintainable, and well-documented
- Lead technical architecture discussions for AI initiatives
- Troubleshoot and resolve integration issues
- Optimize system performance and data flow
- Maintain security and compliance standards
- Drive best practices in software engineering across teams
- Mentor engineers on AI technologies and implementation patterns
- Develop proof-of-concept models for new integrations
- Monitor AI system performance and accuracy
- Refine models based on feedback and usage data
- Work closely with data scientists and platform engineers
- Support deployment and CI/CD pipelines
- Ensure high availability and fault tolerance
- Contribute to technical roadmaps
- Assess emerging AI technologies for applicability
- Improve developer experience for integration tools
- Lead code reviews and system design evaluations
- Define metrics for integration success
- Coordinate with customer-facing teams for solution feedback
- Balance innovation with technical debt management
- Promote reusability across integration components
- Ensure alignment with long-term platform vision
Compensation
Competitive salary and equity package
Work Arrangement
Remote-friendly with flexible scheduling
Team
Collaborative engineering team focused on AI and automation
What We Value
- Technical excellence with a focus on real-world impact
- Curiosity and continuous learning in AI advancements
- Clear communication across technical and non-technical stakeholders
- Ownership of projects from concept to deployment
- Collaborative problem solving and knowledge sharing
Technology Stack
- Python for backend and AI logic
- AWS for cloud infrastructure
- Docker and Kubernetes for deployment
- RESTful and GraphQL APIs
- PostgreSQL for data storage
- TensorFlow or PyTorch for machine learning models
- CI/CD using GitHub Actions or similar
- Monitoring with Prometheus and Grafana
- Natural language processing pipelines
- Event-driven architecture patterns
Available for qualified candidates
