AIMidFull-time

AI Engineer

Build and ship Agentic AI features that integrate with enterprise systems — from retrieval pipelines to multi-step agents — alongside senior engineers and domain experts.

About the Role

At Triomind, we build Agentic AI frameworks that sit alongside enterprise systems to automate complex workflows across facilities, assets, and capital programs. This is a role for engineers who want to ship AI that real institutions depend on — not another demo.

What You'll Do

  • Implement and maintain production LLM and agent pipelines — retrieval, tool use, prompt templates, evaluation harnesses — across client engagements.
  • Build integrations between AI services and enterprise platforms (EAM, CMMS, ERP, data warehouses) via REST, GraphQL, or message-based APIs.
  • Contribute to prompt design, dataset curation, and offline / online evaluation to continuously improve model quality and task success rates.
  • Work closely with senior engineers on system design, code review, and observability for AI workloads.
  • Support clients during pilots: investigate edge cases, iterate on agent behaviour, and help translate feedback into well-scoped improvements.

What We're Looking For

  • 2+ years of professional software engineering experience, including hands-on work with LLMs, RAG pipelines, or ML models in production or near-production environments.
  • Strong Python skills; comfortable with at least one of TypeScript / Node.js, Go, or Java for service development.
  • Practical experience with modern AI tooling (OpenAI / Anthropic / Azure OpenAI APIs, LangChain / LangGraph / LlamaIndex, or equivalent) and vector stores.
  • Good fundamentals in data structures, APIs, SQL, and testing; you write code others can safely extend.
  • Clear written and verbal communication — you can explain trade-offs to teammates and clients.
  • Must be legally authorized to work in Canada.

Nice to Have

  • Exposure to public-sector or regulated-industry projects (government, municipalities, universities, healthcare).
  • Experience with evaluation frameworks (Ragas, DeepEval, custom harnesses) and LLM observability tools.
  • Familiarity with containerization, CI/CD, and cloud deployment (Azure, AWS, or GCP).
  • Open-source contributions or personal projects involving agents, RAG, or fine-tuning.

Tech We Use

PythonTypeScriptLLMsRAGLangGraphPostgreSQLpgvectorFastAPIDockerAzure