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Cybersecurity, IT Services, Managed Security Services, Value-Added Reseller (VAR)

Senior AI Engineer

Toronto, ONHybridFull TimeSeniorCA$175,000–CA$210,000 /yrPosted 8 days ago

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Required skills

  • multi-agent workflows
  • conversational AI
  • retrieval
  • structured data
  • task planning
  • tool use
  • delegation
  • handoffs
  • result synthesis
  • permissions

Nice to have

  • agent and orchestration frameworks
  • plain code
  • Databricks workflows
  • real-time systems
  • streaming systems
  • WebSockets

Role summary

Guidepoint is seeking a Senior AI Engineer to build and scale production AI systems for its research platform. This hybrid role involves designing and implementing multi-agent workflows, ensuring dependable agent behavior, and owning orchestration and state management. You will develop production APIs using Python, build retrieval and search pipelines over knowledge assets, and create evaluation systems for AI performance. The role also includes deploying and operating AI services on cloud platforms like Azure and Databricks, tuning the AI pipeline, and integrating various providers. You will establish engineering best practices for code review, testing, and observability. The ideal candidate has 8+ years of software engineering experience, including 3+ years in production AI/LLM products, with a strong background in Python, cloud-native deployment, and LLM evaluation.

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.

    #LI-SP1

    #LI-HYBRID

    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
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