AI Engineer

18 days ago

Toronto, CanadaSenior

Responsibilities

Design, build, and operate scalable, low-latency backend services and APIs for Generative AI features.
Own the end-to-end lifecycle of AI-powered applications, including development, deployment, monitoring, and production optimization.
Improve RAG pipelines through retrieval optimization, re-ranking, chunking strategies, and prompt engineering.
Integrate LLMs with proprietary knowledge repositories, external APIs, and real-time data streams for copilots and research assistants.
Establish LLMOps practices, automated evaluation, AI observability, monitoring, and performance optimization.
Evaluate prompt engineering methods and model interaction techniques for proprietary and open-source LLMs.
Provide technical leadership through code reviews, mentorship, and design discussions.
Partner with product and business stakeholders to translate user needs into technical requirements and roadmap priorities.

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.
Proven experience designing, building, and scaling distributed, production-grade systems.
Deep expertise in Python, a major backend framework such as FastAPI or Flask, and asynchronous programming such as asyncio.
Experience with RESTful APIs, microservices, testing, CI/CD, observability, monitoring, alerting, high uptime, and zero-downtime deployments.
Hands-on experience deploying and managing applications on Azure, AWS, or GCP using Docker, Kubernetes, and Helm.
At least 2 years of experience building applications using LLMs from providers such as OpenAI, Anthropic, or Google Gemini.
Required experience with RAG, vector-database-based hybrid search, multi-agent systems with tool calls, and prompt engineering.
Experience designing evaluation frameworks for LLM systems, including rubric-based scoring, LLM Judges, or MLflow, with monitoring for performance and drift.
Familiarity with Databricks and Apache Spark for large-scale data processing.
Practical experience with LangChain or LlamaIndex for LLM-powered applications.
Ability to lead complex technical projects and mentor other engineers.

Hybrid position based in Toronto.
Annual discretionary performance bonus eligibility.
Paid time off.
Comprehensive benefits plan.
Company RRSP match.
Development opportunities through LinkedIn Learning.
Interview process includes virtual interviews, an on-site live coding and system design test, technical leadership interview, and reference checks.

1,001-5,000 employees