AI Solution Engineer

Hi LinkedIn! 👋
📢 Hiring: AI Solution Engineer (Level 3)
📍 Montreal, QC – (Hybrid – 3 days onsite)
💼 Contract (Full-Time)
🚨 Note: Only candidates currently based in Canada will be considered.
We’re looking for a hands-on AI Solution Engineer to join a leading financial services firm, focused on building AI-driven prototypes and internal tools that improve business workflows, decision-making, and operational efficiency.
This is a great opportunity for someone passionate about applied AI, experimentation, and building end-to-end solutions using LLMs and modern AI frameworks.
✅ Must Have:
• Strong hands-on experience with Python
• Proven ability to build end-to-end solutions from scratch (projects, POCs, or production work)
• Experience integrating APIs and structured data into applications
• Solid understanding of LLM concepts (prompt engineering, token usage, cost optimization)
• Experience building RAG (Retrieval Augmented Generation) solutions
• Strong problem-solving skills and ability to work in fast-paced environments
• Excellent communication skills with both technical and non-technical stakeholders
⭐ Nice to Have:
• Experience with NotebookLM / RAG tooling or similar Python-based frameworks
• Exposure to Claude / Anthropic APIs or other LLM platforms
• Experience with vector databases (Pinecone, Chroma, etc.)
• Familiarity with LangChain or similar orchestration frameworks
• Experience building AI agents or workflow automation tools
• Understanding of LLM governance (monitoring, logging, cost control)
• Active GitHub projects showcasing applied AI work
💡 Key Responsibilities:
• Design and build AI-driven POCs and internal tools for real business problems
• Develop rapid prototypes using Python, APIs, and data pipelines
• Integrate LLMs into use cases like document analysis, summarization, and advisory tools
• Build RAG-based solutions leveraging internal knowledge sources
• Collaborate with business stakeholders to refine requirements
• Work with technology teams to transition POCs into production-ready solutions
• Contribute to architecture and implementation discussions
📦 Key Deliverables:
• AI-powered prototypes and internal tools
• RAG-based applications and knowledge solutions
• Workflow automation and decision-support systems
• Technical documentation and handoff for production scaling