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Senior

Intermediate Software Engineer - Artificial Intelligence (AI)

Confirmed live in the last 24 hours

Tucows

Tucows

Toronto, CA
Hybrid
Posted April 7, 2026

Job Description

Tucows Domains is the world’s largest wholesale domain registrar, responsible for maintaining the health, neutrality, and openness of an important—but largely invisible part of the Internet: the domain name system (DNS).

As part of Tucows—one of the world’s largest Internet companies—Tucows Domains has a rich history of helping make the Internet better, operating globally under the Ascio, Enom, Hover and OpenSRS brands.

We embrace a people-first philosophy that is rooted in respect, trust, and flexibility. We believe that whatever works for our employees is what works best for us. It’s also why the majority of our roles are remote-first, meaning you can work from anywhere you can connect to the Internet! Today, over one thousand people from over 20 countries are part of our team.

If this sounds exciting to you, join the herd! 

About the Opportunity

We’re looking for a passionate Intermediate Software Engineer specializing in Artificial Intelligence (AI) to join our growing team. In this role, you’ll help shape and build innovative AI-powered systems that transform how users interact with domain-related tools and services. You’ll work both with your team of forward-thinking engineers and with colleagues across business functions to prototype, develop, and deploy intelligent solutions using open-source models and modern infrastructure.

This is a hybrid position, requiring 3 days a week at our Toronto office for collaboration. 

What You’ll Do

  • Design and build AI-driven features for our domain services platform using Python and Golang.
  • Integrate and fine-tune open-source models such as LLaMA 3.2 and similar cutting-edge architectures via tools like Ollama.
  • Research, evaluate, and implement emerging AI technologies that align with our vision for smarter, more intuitive products and services.
  • Collaborate with internal stakeholders and fellow engineers to rapidly prototype and iterate on machine learning and LLM-based features.
  • Contribute to a modern AI development stack, ensuring scalability, performance, and ethical usage of models.
  • Actively participate in the open-source ecosystem and bring relevant tools and techniques back to the team.

Key Skills and Experience

`Core Engineering

  • Bachelor’s degree in Software Engineering, Computer Science, or a related field
  • 3+ years of professional software engineering experience in production environments
  • Strong proficiency in Python and Golang
  • Solid foundation in software design principles, patterns, and service-oriented architecture
  • Experience contributing to scalable systems and component-level architecture
  • Ability to design and build RESTful APIs for model serving and AI-enabled workflows
  • Working knowledge of relational/SQL databases (preferably PostgreSQL) and data modeling for AI use cases

LLM & AI Application Engineering

  • Strong understanding of modern LLM concepts, including transformer architectures and attention mechanisms
  • Hands-on experience adapting and deploying open-source models (e.g., LLaMA, Mistral, Mixtral) using tools like Ollama or Hugging Face Transformers
  • Experience with fine-tuning techniques (e.g., LoRA, QLoRA, PEFT) for domain-specific adaptation
  • Proficiency in prompt engineering (few-shot, chain-of-thought, structured outputs)
  • Familiarity with model serving patterns for efficient, scalable inference

RAG & Knowledge Systems

  • Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines end-to-end
  • Hands-on experience with vector databases (e.g., pgvector, Pinecone, Weaviate)
  • Familiarity with embedding models, chunking strategies, and semantic search patterns
  • Understanding of data pipelines for ingestion, transformation, and inference result storage

Agentic Systems & Tooling

  • Familiarity with Model Context Protocol (MCP) server design patterns
  • Experience with agent orchestration frameworks (e.g., LangChain, LangGraph)
  • Understanding of tool use, function calling, and
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