- Lead design and development of applied AI systems (RAG pipelines, LLM integrations, APIs).
- Ensure safe, reliable AI outputs with guardrails, monitoring, and evaluation.
- Prototype quickly, then scale to production with maintainable code.
- Mentor engineers on AI best practices and safe adoption.
- Partner with product and engineering leadership to collaborate on project timelines and scope for AI initiatives.
- Make effective trade-offs between technical debt and short-term delivery of value.
- Run experiments and rapidly prototype to quickly assess tools and prospective features.
- Use AI to effectively deliver high-quality, efficient, and maintainable code, setting standards and best practices for the engineering team.
- Collaborate closely with cross-functional teams to integrate AI solutions into existing and new features and applications.
- Contribute to the development of foundational AI capabilities, such as RAG/vector-database solutions and agentic workflow solutions.
- Create solutions for continuously improving AI solutions through monitoring, analytics and user feedback.
- Support the ability to easily evaluate AI performance against various models and prompts.
- Troubleshoot and resolve intricate technical issues related to AI implementations.
- Mentor and guide other engineers, fostering their adoption and appropriate use of AI.
- Support innovation through researching and assessing the latest AI advances on an ongoing basis.
- Contribute to comprehensive testing strategies, including unit testing, integration testing, and continuous integration practices.
- Develop and maintain documentation of AI solutions and related processes and best practices.
- Support the production operations of the applications by participating in technical support and release duty rotations.