Senior AI Engineer

Overview:

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.
• 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:

Responsibilities

• 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.
• 6+ years of software engineering, with 2+ years building LLM-powered products in production (not notebooks): orchestration, RAG/grounding, structured outputs, eval-driven iteration.
• 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.
• 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.