Junior Software Developer contributing to full - stack development of web - based applications in a geospatial intelligence company. Collaborating in an agile team with exposure to innovative projects.
About the role
- Intermediate Software Engineer specializing in AI to build innovative domain services features for Tucows Domains. Collaborating with engineers to develop AI-powered solutions.
Responsibilities
- 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.
Requirements
- 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
- 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
- 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
- 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 multi-step reasoning in LLM workflows
- Experience with LLM evaluation frameworks (e.g., RAGAS, promptfoo, or custom pipelines)
- Familiarity with observability and tracing tools (e.g., LangSmith, Helicone)
- Comfort with structured logging, metrics, and alerting for AI workloads
- Experience with containerization and cloud-native deployment (preferably AWS)
- Familiarity with Kubernetes or EKS for scaling model-serving workloads
- Understanding of GPU considerations for inference (quantization, batching, memory trade-offs)
- Active interest in the open-source AI ecosystem
- Strong collaboration and communication skills across technical and business teams
- Enthusiasm for emerging AI technologies with a practical, delivery-focused mindset.
Benefits
- Health insurance
- Professional development
- Flexible work arrangements
- Paid time off
- Generous compensation
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