Location: Remote (Canada Eastern time zones)
Type: Contract (3 months, strong possibility of extension)
Start: Interviews rolling in the next 1–2 weeks; start ASAP

WHAT WE ARE LOOKING

We’re looking for a hands-on AI Engineer who loves hard problems and learns fast. You’ll turn GenAI ideas into reliable, secure enterprise features, combining non-deterministic LLM behavior with solid software engineering and MLOps. Healthcare experience is a plus, not a must. We value grit, practical GenAI builds, and clear thinking over perfect resumes.
As an AI Engineer, you’ll help us take our GenAI platform to the next level. If the concept of building something from scratch and improvising an existing data pipeline that will fast-track the success of a high-growth business and owning it end-to-end is something that excites you then you are exactly the right person that we’re looking for! The ideal candidate is an individual who loves to solve business problems, understands a diversity of perspectives is the best way to solve problems, has a passion for learning, and enjoys exploring new technologies to stay ahead of the curve with continued learning and growth of the utmost importance. Refined communication skills, a positive attitude, and team orientation are necessary.

MINIMUM QUALIFICATIONS

• ~3+ years in two or more of: data engineering, ML engineering, or software engineering (or equivalent practical experience).
• Practical experience delivering GenAI/LLM applications (beyond toy demos).
• Familiarity with GenAI frameworks (e.g., LangChain, A2A/agents, MCP, or similar).
• Solid MLOps/DevOps exposure (model packaging, deployment, monitoring, CI/CD).
• Proficiency on one major cloud (AWS or Azure preferred; GCP welcome).
• Understanding of MLOps concepts: continuous training, continuous monitoring, and continuous improvement of ML/AI systems.

• Collaborate with product & engineering to ship GenAI features that meet real business needs.
• Combine LLMs with deterministic services to build robust GenAI applications (RAG, tools/agents, workflows).
• Design and maintain evaluation frameworks to measure model quality; produce regular accuracy/quality reports.
• Iterate with prompt engineering, software fixes, RL/RLAIF-style tuning where applicable.
• Write clean, maintainable code and automated tests.
• Build/operate MLOps: data pipelines, CI/CD for models, monitoring, drift/quality alerts, continuous improvement loops.
• Handle data responsibly: respect PII/data-security policies across train/test/inference workflows.