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 on them.
Integrate third-party AI and voice vendors behind clean abstractions so providers stay swappable.
Partner with product and compliance stakeholders to encode domain rules into the system.
Set engineering patterns for a growing team: code review, observability, incident habits.

What You Have

6+ years of software engineering, with 2+ years building LLM-powered products in production (not notebooks): orchestration, RAG/grounding, structured outputs, eval-driven iteration.
A Bachelor’s degree in Computer Science, Engineering, or a related technical field with 6+ years of professional experience; or a Master’s degree with 4+ years of professional experience in backend software engineering and Generative AI. This must include a proven track record of designing, building, and scaling distributed, production-grade system
Strong TypeScript or Python; comfortable working across both.
Real-time or streaming systems experience: WebSockets, WebRTC, audio pipelines, or comparable low-latency work.
Cloud-native production experience: containers, Kubernetes, CI/CD, observability. Azure a plus.
Familiarity with agent/LLM frameworks such as LangGraph, LangChain, or CrewAI — hands-on exposure is enough; we care that you know the landscape and when plain code beats a framework.
Working familiarity with vector databases and embedding-based retrieval.
Pragmatic product judgment: you scope prototypes, kill weak approaches with data, and ship.

Nice-to-have

Voice AI: telephony platforms, conversational agent frameworks, STT/TTS, turn-taking/VAD.
Knowledge graphs or large-scale search/retrieval systems.
Regulated or compliance-sensitive domains (healthcare, finance).
Eval harnesses or LLM-as-judge systems.

What We Offer:

The annual base salary range for this position is 175,000-210,000 CAD. Additionally, this position is eligible for an annual discretionary bonus based on performance.

You will also be eligible for the following benefits:

Paid time off
Comprehensive benefits plan
Company RRSP match
Development opportunities through the LinkedIn Learning platform

About Guidepoint:

Guidepoint powers end-to-end research workflows for the world’s best research teams.

Backed by a global network of more than 2 million experts and over 1,600 employees, Guidepoint delivers real-time access to expertise, primary research, and actionable knowledge that help organizations make informed decisions. Through consultations, surveys, events, proprietary content, and AI-enabled tools, we support every stage of the decision-making process—from developing hypotheses and gathering insights to validating assumptions and building conviction for critical decisions.