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Guidepoint

Senior AI Engineer

Toronto, ON, Canada

$175,000 - $210,000 CAD

Apply About employer

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Job details

Pay: $175,000 - $210,000 CAD
Experience: 8+ years of software engineering experience, including 3+ years building and operating production AI or LLM-powered products.
Posting language: English
Posted: Oct 29, 2026

Original job description

Overview

Guidepoint is investing in applied AI across its research platform. We are building production AI systems that help research teams interact with our global expert network and proprietary knowledge assets. These systems combine multi-agent workflows, conversational AI, retrieval, and structured data to support reliable, high-quality research experiences.

We are looking for a hands-on Senior AI Engineer to take working prototypes into production and shape the architecture as the product grows. You will have broad ownership across agent design, evaluation, APIs, and cloud deployment, working closely with product, engineering, and compliance stakeholders.

This is a hybrid role based in our Toronto office.

What You’ll Do:

Build multi-agent workflows: Design and ship systems where specialized agents collaborate to complete research tasks, including task planning, tool use, delegation, handoffs, and result synthesis.
Make agent behavior dependable: Enforce permissions, domain rules, workflow boundaries, and output requirements in application code. Design for safe tool use, clear failure handling, and human review when needed.
Own orchestration and state: Build reliable multi-turn workflows with explicit state management, memory and context strategies, timeouts, retries, and recovery from partial failures.
Develop production APIs: Build and maintain Python services and APIs that connect agents to Guidepoint products, internal data, and third-party providers.
Ground responses in trusted data: Build retrieval and search pipelines over large internal knowledge assets. Return structured, traceable results and make it clear which sources support an answer.
Build the evaluation system: Define and maintain evaluations for task success, factual accuracy, groundedness, completeness, policy adherence, latency, and cost. Use representative test sets, regression checks, and production feedback to guide improvements and rollout decisions.
Deploy and operate AI services: Own services from design through production deployment and operations, using containers, Kubernetes, Azure, and Databricks where appropriate.
Tune the full AI pipeline: Select models and tools, engineer context, define structured outputs, and manage tradeoffs among quality, latency, reliability, and cost.
Integrate providers cleanly: Put model, retrieval, and voice providers behind clear interfaces so the system can evolve as products and requirements change.
Set engineering practices: Establish patterns for code review, testing, observability, incident response, and production readiness as the team grows.

What You Have:

8+ years of software engineering experience, including 3+ years building and operating production AI or LLM-powered products.
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 systems.
Demonstrated experience designing and shipping multi-agent or agentic workflows in production, including orchestration, tool use, state management, and failure handling.
Strong Python skills and experience building production APIs and backend services. TypeScript experience is useful.
Hands-on experience with cloud-native deployment, including containers, Kubernetes, CI/CD, and production observability.
Experience working with Azure and/or Databricks in production environments.
Practical experience with LLM evaluation: creating test sets, measuring quality and accuracy, analyzing failures, and using results to improve systems or gate releases.
Experience with retrieval-augmented generation, embeddings, vector databases, or large-scale search systems.
Understanding of model and workflow risks, including unsupported answers, prompt injection, unsafe tool calls, and data access boundaries - and experience addressing them through system design and code.
Experience operating services with production requirements for reliability, latency, and cost.
Product judgment: you can scope a useful first version, compare approaches with evidence, and ship iteratively.

Nice to Have:

Experience with agent and orchestration frameworks such as LangGraph, LangChain, or similar, along with the judgment to use plain code when it is a better fit.
Experience with Databricks, including its data, machine learning, or deployment workflows.
Real-time or streaming systems experience, such as WebSockets, WebRTC, audio pipelines, or other low-latency services.
Voice AI experience, including telephony platforms, speech-to-text, text-to-speech, turn-taking, or voice-agent frameworks.
Knowledge graphs or large-scale information retrieval experience.
Experience in regulated or compliance-sensitive industries such as healthcare or finance.
Experience with LLM-as-judge methods, human review workflows, or production monitoring for AI quality.

What We Offer:

The annual base salary range for this position is 175,000-210,000. 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.

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.

This job posting is for an existing vacancy. The salary offered will be based on the successful candidate’s skills, experience, qualifications, and location.

Compensation

$175,000 - $210,000 CAD

Apply About employer

More about this job

Responsibilities

The Senior AI Engineer will design and deploy production-grade multi-agent AI systems and orchestrate complex workflows. They will also manage the full AI pipeline, including evaluation, API development, and cloud-based operations to ensure system reliability and performance.

Requirements

Candidates must have over 8 years of software engineering experience with a proven track record in building production-grade AI or LLM-powered systems. A Bachelor's or Master's degree in a technical field is required, along with strong proficiency in Python and cloud-native deployment.

Skills

Python
Generative AI
LLM
Multi-agent workflows
Kubernetes
Azure
Databricks
API development
Retrieval-augmented generation
Vector databases
System architecture
Cloud-native deployment
CI/CD
Observability
Prompt engineering
Software engineering

Benefits

Paid Time Off
Comprehensive benefits plan
Company RRSP Match
Development opportunities

Visa sponsorship

Not detected in the job text

Keywords

Senior AI Engineer
Generative AI
LLM
Multi-agent
Python
Kubernetes
Azure
Databricks
API
Retrieval-augmented generation
Vector databases
CI/CD
Observability
Cloud-native
Distributed systems
State management
Prompt engineering
LangGraph
LangChain
Machine learning
Software engineering
Data engineering
System architecture
Production readiness
Latency optimization
Reliability engineering
Research platform
Expert network
Compliance
Data access

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