Role overview

Guidepoint is seeking an experienced AI Engineer to join its Toronto-based Data & AI team. This hybrid role is central to building modern data infrastructure and developing next-generation Generative AI capabilities that will power Guidepoint’s research enablement platform and AI-driven data products. You will take ownership of designing and scaling production-grade AI systems, from large language model applications to complex agentic systems, while championing responsible AI practices across the organization.

Key responsibilities

Design, build, and operate scalable backend services and APIs that deliver Generative AI features, including retrieval-augmented generation pipelines and multi-agent systems.
Own the complete lifecycle of AI-powered applications, from system design through development, deployment using CI/CD practices, monitoring, and production optimization on platforms such as Databricks and Azure Kubernetes Service.
Continuously improve retrieval and generation quality in RAG systems through techniques including retrieval optimization, re-ranking strategies, advanced chunking, and prompt engineering to reduce hallucination.
Engineer solutions that integrate large language models with proprietary knowledge repositories, external APIs, and real-time data streams to create intelligent copilots and research assistants.
Establish and implement LLMOps best practices and system monitoring in collaboration with data science and engineering teams, utilizing automated evaluation frameworks and AI observability tools.
Evaluate and implement advanced prompt engineering methods and model interaction techniques to optimize performance and safety across proprietary and open-source language models.
Provide technical leadership to junior engineers through code reviews, mentorship, and design discussions to elevate team engineering standards and shape the future AI product roadmap.

Requirements

A Bachelor’s degree in Computer Science, Engineering, or related technical field with 6 or more years of professional backend software engineering experience, or a Master’s degree with 4 or more years of professional experience, with proven track record designing and scaling distributed production systems.
Deep expertise in Python, a major backend framework such as FastAPI or Flask, and asynchronous programming patterns, with proficiency in RESTful API design, microservices architecture, testing, CI/CD practices, observability, and zero-downtime deployments.
Hands-on experience deploying and managing applications on a major cloud platform (Azure preferred, AWS or GCP acceptable) using Docker containerization and Kubernetes orchestration with Helm.
At least 2 years of direct experience building production applications leveraging large language models from providers such as OpenAI, Anthropic, or Google Gemini, with demonstrated expertise in RAG systems, vector databases, multi-agent systems with tool calling, and prompt engineering.
Experience designing and implementing evaluation frameworks for LLM-based systems, including rubric-based scoring, LLM Judges, and tools like MLflow for performance monitoring.
Familiarity with large-scale data processing platforms such as Databricks and Apache Spark, and practical experience with frameworks like LangChain or LlamaIndex.
Demonstrated ability to lead complex technical projects and mentor other engineers.

What makes this role worth considering

This position offers the opportunity to work on cutting-edge Generative AI systems at a company investing strategically in its Toronto Technology Hub as a center for AI innovation. You will collaborate with a technically talented team focused on building responsible AI capabilities while maintaining the operational excellence required for production-grade systems serving real users.

The role includes a competitive salary range of $135,000 to $210,000 annually, along with performance-based discretionary bonuses, paid time off, comprehensive benefits, company RRSP matching, and professional development support.