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Job Description & How to Apply Below
Guidepoint seeks an experienced AI Engineer as an integral member of the Toronto‑based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting‑edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next‑Gen research enablement platform and data products.
This role demands exceptional leadership and technical prowess to drive the development of next‑generation research enablement platforms and AI‑driven data products. You will develop and scale Generative AI‑powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best‑in‑class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state‑of‑the‑art tools across all of Guidepoint’s products.
Guidepoint’s Technology team thrives on problem‑solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge‑sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI‑enabled products optimize the seamless delivery of our services.
What You’ll Do
Architect and build scalable, low‑latency backend services and APIs that serve Generative AI features, from retrieval‑augmented generation (RAG) pipelines to complex agentic systems.
Own the end‑to‑end lifecycle of AI‑powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production environments like Databricks and Azure Kubernetes Service (AKS).
Continuously improve retrieval and generation quality through techniques such as retrieval optimization (tuning k‑values, chunk sizes), using re‑rankers, advanced chunking strategies, and prompt engineering for hallucination reduction.
Engineer solutions that seamlessly combine LLMs with proprietary knowledge repositories, external APIs, and real‑time data streams to create powerful copilots and research assistants.
Collaborate with data science and engineering teams to establish and implement best practices for LLMOps, including automated evaluation using frameworks such as LLM Judges or MLflow, AI observability, and system monitoring.
Systematically evaluate and apply advanced prompt engineering methods (e.g., Chain‑of‑Thought, ReAct) and other model interaction techniques to optimize the performance and safety of proprietary and open‑source LLMs.
Provide technical leadership to junior engineers through rigorous code reviews, mentorship, and design discussions, elevating the team’s engineering standards.
Partner closely with product and business stakeholders to translate user needs into technical requirements, define priorities, and shape the future of our AI product offerings.
What You’ll Bring
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. Requires a proven track record of designing, building, and scaling distributed, production‑grade systems.
Deep expertise in Python, a major backend framework (e.g., FastAPI, Flask), and asynchronous programming (e.g., asyncio). Proficiency in designing RESTful APIs, microservices, and the complete operational lifecycle, including comprehensive testing, CI/CD (e.g., ArgoCD), observability, monitoring, and zero‑downtime deployments.
Hands‑on experience deploying and managing applications on a major cloud platform (Azure preferred, AWS/GCP acceptable) using containerization (Docker) and orchestration (Kubernetes, Helm).
2+ years of experience building applications that leverage large language models from providers like OpenAI, Anthropic, or Google Gemini. Direct experience with modern LLM patterns such as RAG,…
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