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About the Role
Guidepoint is building conversational AI systems that interact with its global expert network, combining real-time voice, LLM orchestration, and retrieval over large proprietary datasets. You will be an early engineer on a new initiative, taking 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.
Responsibilities
- Develop real-time conversational AI systems, including voice- and chat-based agents with low-latency requirements.
- Build agentic systems for LLM orchestration that pursue goals over multi-turn conversations with behavioral guarantees.
- Manage multi-channel, stateful user interactions that transition seamlessly across channels.
- Connect LLM systems to internal knowledge assets for retrieval and grounding, ensuring structured data flow.
- Create evaluation infrastructure to measure conversational quality against human baselines and gate rollouts.
- Own services end-to-end, including design, implementation, deployment on Kubernetes/Azure, and operations.
- Design and tune LLM pipelines, focusing on context engineering, structured outputs, model selection, and cost/latency tradeoffs.
- Build the evaluation loop and treat quality as a quantifiable metric, defining metrics, running experiments, and gating releases.
- Integrate third-party AI and voice vendors behind clean abstractions for swappability.
- Partner with product and compliance stakeholders to encode domain rules into the system.
- Set engineering patterns for a growing team, including code review, observability, and incident management.
- 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.
Requirements
- 6+ years of software engineering experience.
- 2+ years building LLM-powered products in production.
- Experience with LLM orchestration, RAG/grounding, structured outputs, and eval-driven iteration.
- Strong proficiency in TypeScript or Python, comfortable working across both.
- Experience with real-time or streaming systems such as WebSockets, WebRTC, or audio pipelines.
- Cloud-native production experience with containers, Kubernetes, CI/CD, and observability.
- Familiarity with agent/LLM frameworks like LangGraph, LangChain, or CrewAI.
- Working familiarity with vector databases and embedding-based retrieval.
- Pragmatic product judgment, including scoping prototypes, data-driven decision-making, and shipping products.
- Experience with Azure is a plus.
- Experience with voice AI, including telephony platforms, conversational agent frameworks, STT/TTS, and turn-taking/VAD is a plus.
- Experience with knowledge graphs or large-scale search/retrieval systems is a plus.
- Experience in regulated or compliance-sensitive domains (healthcare, finance) is a plus.
- Experience with eval harnesses or LLM-as-judge systems is a plus.
Skills
- TypeScript
- Python
- LLM orchestration
- RAG/grounding
- Structured outputs
- Eval-driven iteration
- WebSockets
- WebRTC
- Audio pipelines
- Low-latency systems
- Containers
- Kubernetes
- CI/CD
- Observability
- Agent frameworks
- LLM frameworks
- LangGraph
- LangChain
- CrewAI
- Vector databases
- Embedding-based retrieval
- Voice AI
- Telephony platforms
- Conversational agent frameworks
- STT/TTS
- Turn-taking/VAD
- Knowledge graphs
- Large-scale search/retrieval systems
- Regulated domains
- Compliance-sensitive domains
- Eval harnesses
- LLM-as-judge systems
Location
- Toronto, Canada
Work Type
- Hybrid
Experience Level
- 6+ years of software engineering
- 2+ years building LLM-powered products in production
Salary/Compensations
- 175,000-210,000 CAD
Benefits
- Paid time off
- Comprehensive benefits plan
- Company RRSP match
- Development opportunities through the LinkedIn Learning platform
- Annual discretionary bonus based on performance
About the Company
- 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.
- At Guidepoint, our success relies on the diversity of our employees, experts, and clients, which enables us to foster meaningful connections and a broad range of perspectives.
- We are committed to creating an inclusive and welcoming environment where individuals of all backgrounds, identities, and experiences can contribute and succeed.