LLM · conversational AI
Overview
Guidepoint is investing in applied AI across its research platform. We're building conversational AI systems that interact with our global expert network at scale — combining real-time voice, LLM-driven orchestration, and retrieval over large proprietary datasets.
You'll be an early engineer on a new initiative: taking working prototypes to production and shaping the architecture as the product scope grows. This is a hands-on role with broad ownership, close to product and leadership.
This role is hybrid in our Toronto Office
What You'll Do
- Real-time conversational AI: voice- and chat-based agents with low-latency requirements, built on modern telephony and real-time media infrastructure.
- LLM orchestration: agentic systems that pursue goals over multi-turn conversations, with hard behavioral guarantees enforced in code — not just prompts.
- Multi-channel session management: stateful user interactions that move seamlessly across channels.
- Retrieval and grounding: connecting LLM systems to large internal knowledge assets, with structured data flowing back.
- Evaluation infrastructure: measuring conversational quality against human baselines and gating rollouts on metrics, not vibes.
- Own services end-to-end: design, implementation, deployment (Kubernetes/Azure), and operations, including latency-sensitive real-time workloads.
- Design and tune LLM pipelines: context engineering, structured outputs, model selection, cost/latency tradeoffs across vendors.
- Build the eval loop and treat quality as a number: define metrics, run experiments, gate releases