Role overview
- You won’t be training LLMs or building foundation models from scratch.
- You won’t be focused on academic research or publishing papers.
- You won’t be working in isolation — this role is highly collaborative with product and engineering teams.
- You won’t be building experimental prototypes only; you’ll be delivering production-ready, customer-facing AI features.
This role is about applied AI engineering: using the best tools available (Vertex AI, OpenAI APIs, RAG pipelines, vector databases) to solve real-world customer problems.
What you'll work on
- Design and implement AI-powered workflows using Vertex AI Agents, Pydantic, Bland.ai and other orchestration frameworks.
- Integrate LLMs into customer workflows (e.g., natural language scheduling, eligibility insights, onboarding automation).
- Build scalable APIs and services that serve AI features reliably in production.
- Implement evaluation, monitoring, and safety checks to ensure AI systems are accurate, fair, and trustworthy.
- Collaborate with Product and Engineering to identify practical, high-impact AI use cases.
- Document integration patterns and best practices for AI deployment.
- Provide technical leadership and mentorship on AI integrations.
What we're looking for
- Experience with retrieval-augmented generation (RAG) pipelines and vector databases.
- Exposure to compliance-aware AI systems in regulated industries.
- Contributions to open-source AI/LLM frameworks.
- Knowledge of monitoring/evaluation techniques for AI systems.
Tags & focus areas
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