Senior AI Engineer

About the role

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.