About TensorOps

TensorOps is a boutique AI consultancy that bridges strategy and execution, we design and ship production-grade AI systems for enterprise clients, from Fortune 500 companies to fast-growing unicorns. Our work spans agentic AI, LLM fine-tuning, RAG systems, and ML-driven products, deployed on AWS, GCP, and Azure. We've shipped AI systems impacting 200M+ end users daily, partnered with 11 unicorns and NASDAQ-listed companies (including Notion, ServiceNow, JFrog, Seeking Alpha, Armis, and GoCardless), and get 95% of validated ideas into production within two months. We're Google Cloud, AWS, and Cloudflare partners, and we're 100% remote by design.

About the role

- Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients
- Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration
- Work directly with client engineering and product teams to translate business needs into scoped, shippable technical solutions
- Mentor and support other ML engineers on the team — code reviews, technical guidance, and knowledge sharing
- Help shape internal best practices, tooling, and technical standards as the team grows
- Represent TensorOps technically in client conversations, workshops, and (optionally) at industry conferences

You’ll be part of a supportive, fast-growing team that values autonomy, open communication, and continuous learning.

Requirements





- 2+ years of professional experience in Machine Learning, AI Engineering, or a related role (Mid-level) / 5+ years for Senior
- Strong hands-on skills in Python, writing clean, effective, well-documented, production-quality code
- Proven experience designing, training, optimizing, and deploying ML models independently (e.g., PyTorch, TensorFlow, Scikit-learn)
- Experience building GenAI & LLM systems: RAG pipelines, chatbot architectures, and applications using tools like LangChain
- Familiarity with MLOps & production ML practices: model versioning, monitoring, CI/CD for ML workflows
- Experience deploying and scaling ML systems on AWS, GCP, or Azure
- Strong performance optimization and debugging skills (diagnosing complex issues and improving system reliability and efficiency)
- Experience working with stakeholders or clients is a plus

What We Offer

- 100% Remote Work : no mandatory office days, work from wherever
- Funded certifications: fully paid AWS and GCP professional certifications
- Dynamic, High-Impact Projects : Work on cutting-edge ML and GenAI solutions across diverse industries
- International Clients : Collaborate with global organizations and solve real-world challenges at scale
- Urban Sports Club Membership : Supporting your physical and mental wellbeing
- Monthly Bolt Credits : For rides
- Company Events & Offsites : Regular team gatherings to connect, collaborate, and celebrate

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📌 Mid/Senior AI Engineer (Toronto)
🏢 TensorOps
📍 Toronto