What you’ll do
- Meet with client executives and teams to find high-value AI opportunities and separate real use cases from hype
- Assess data readiness, workflows, risks, and ROI, and turn findings into clear roadmaps and business cases
- Design and prototype solutions using LLMs, retrieval-augmented generation (RAG), agents, classification, forecasting, and computer vision
- Build proofs of concept and production pilots with Python and platforms such as Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI, Anthropic, or Hugging Face
- Work with data engineers and developers to connect solutions to client systems, data pipelines, and APIs
- Evaluate models rigorously: accuracy, cost, latency, bias, and safety, with testing methods clients can trust
- Guide responsible AI practice: privacy (PIPEDA and provincial laws), governance, security, and human oversight
- Translate technical tradeoffs into plain language for executives, and business needs into technical requirements for engineers
- Run workshops, demos, and training sessions that help client teams adopt AI and actually use it
- Support change management so new tools stick after the project ends
- Contribute to proposals, reusable assets, and internal research, and share what you learn with the team
Find your level
- Level Typical experience What you’ll own
- AI Consultant I-0-2 years : Prototyping, data analysis, research, documentation, and client support, learning on live projects with a dedicated mentor
- AI Consultant II - 2-5 years : Owning workstreams end to end, leading solution design, running client workshops, managing day-to-day client relationships, mentoring
- Senior AI Consultant - 5+ years : Leading engagements, shaping AI strategy for clients, scoping and selling work, setting technical and ethical standards, developing the team
- Promotion criteria are written down at every level, with regular career conversations. You can grow as a hands-on technical expert, toward strategy and leadership, or into practice management.
What you bring
Must have
- Solid foundations in data and AI: Python, SQL, and an understanding of machine learning and modern LLM techniques
- Hands-on experience building something with AI, through a job, internship, coursework, or a serious personal project
- Clear communication: you can explain a model to a CFO and a business problem to an engineer
- Curiosity about how businesses work and a practical, results-first mindset
- Comfort with ambiguity, since client problems rarely arrive well defined
- Degree or diploma in Computer Science, Data Science, Engineering, Statistics, Mathematics, Business Analytics, or a related field, or equivalent experience. A strong portfolio counts as much as a degree.
- Legal authorization to work in Canada (citizens, permanent residents, and valid work permit holders)
Nice to have (not required)
- Experience with prompt engineering, RAG, vector databases, agent frameworks (LangChain, LlamaIndex), or fine-tuning
- Cloud experience on Azure, AWS, or GCP, and familiarity with MLOps and deployment practices
- Consulting, client-facing, or customer-success experience
- Certifications: Azure AI Engineer, AWS Machine Learning, Google Professional ML Engineer, or similar
- Knowledge of AI governance and risk frameworks (NIST AI RMF, ISO/IEC 42001) and Canadian privacy and AI regulation
- Industry background in [financial services, healthcare, public sector, retail, energy, manufacturing, legal, etc.]
- Fluency in French, an asset for clients in Quebec or bilingual organizations [if applicable]
- GitHub projects, Kaggle work, published writing, or demos you can show
