Senior AI Engineer is a full time role with Tangerine, where you’ll lead the execution and delivery of tech solutions that support Tangerine’s AI platform. In this role, you’ll design, build, and keep evolving scalable AI/ML infrastructure, with MLOps plus LLMOps pipelines model training, deployment, and monitoring.
You’ll also cover governance, Retrieval-Augmented Generation (RAG) and those intelligent-agent capabilities that make things feel a bit more, you know, proactive. You’ll partner up with tech, risk, analytics, data, and business teams to translate the AI strategy into dependable technical outcomes. This position is kind of a mix: hands‑on AI engineering, technical leadership, platform architecture, operational rigor, and mentoring other engineers, not just telling them what to do, but helping them get there.
Company Name : Tangerine
Location : Toronto, Ontario, Canada
Salary: $104498 per year
Job Type : Full-time
Start Date: As soon as possible
Benefits: Tangerine benefits, career‑development opportunities, thousands of online and in‑person learning courses, savings and investment support, workspace flexibility, and an inclusive and diverse work environment.
Job Description Senior AI Engineer, is a full time role with Tangerine, based in Toronto Ontario.
The real focus is on engineering ,and shipping Tangerine’s AI platform capabilities that help power applications, business workflows, and client journeys.
You’ll be doing a mix of scalable setup and hands on tooling work for AI/ML model training, inference, RAG, context engineering, intelligent agents,



and those usual AI pipelines that keep everything moving.
Sometimes it’s more built than refined, if that makes sense.
This position also brings technical leadership into the picture, along with delivery execution, platform reliability, security, governance, monitoring, and a lot of cross teaming work across technology and business groups, both of which can move at different speeds.
Responsibilities Lead the successful engineering delivery of Tangerine’s AI platform, including MLOps and LLMOps pipelines, plus model training, deployment, monitoring, and governance.
Work with technology, and risk, analytics, data, as well as business teams.
Must deliver the AI platform strategy, in a way that actually lands.
Mentor junior and intermediate engineers, and show up in technical leadership forums, even when the conversation is a bit rough around the edges.
Link up with the AI and Agentic Development squads to ensure this platform truly supports building AI applications, not just flashy demos that sort of look excellent. Work with them on the architecture so it fits real use‑cases, with the same energy for the messy stuff too.
Design and implement the usual real‑time, batch, and hybrid AI pipelines, because things should not break when volume spikes . Like,



you know, the moment it actually matters. Build for scale, and make sure the flow stays steady even when load gets weird.
Push solid engineering habits across coding, architecture, DevOps, CI/CD, observability, automated testing, and incident response . Reliability should not be a wish, it should be engineered.
Keep the platform working even when things get messy: resilience, dependable behavior, monitoring that doesn’t just “look around”, proactive issue detection, and then real root‑cause analysis, not the band‑aid , symptom chase.
Make sure the AI platform runs in a secure, compliant, and risk‑aware manner, with guardrails that genuinely hold up under pressure, not ones that quietly fail when asked the wrong way.
Requirements A university degree in Computer Engineering, Computer Science, Technology, or a very close equivalent from experience.
8+ years of work experience in Software, Data, ML, or AI Engineering.
2+ years of technology leadership experience, like Senior or Staff Engineer, where you were expected to guide things.
You’ve delivered complex ML/AI platforms that had to stay highly available, and you did it at real scale.
Hands‑on work with Machine Learning , Generative AI , agentic AI, RAG pipelines, and feature stores, where you have actually shipped parts of it not only reviewed docs.
Deep experience in cloud‑native AI/ML across environments like GCP, Azure, or AWS, and not just one sandbox . You’ve worked the problems that show up in production too.

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📌 Senior AI Engineer – Tangerine (Ontario)
🏢 Tobermory
📍 Ontario