Artificial Intelligence Consultant

What you’ll doMeet with client executives and teams to find high-value AI opportunities and separate real use cases from hypeAssess data readiness, workflows, risks, and ROI, and turn findings into clear roadmaps and business casesDesign and prototype solutions using LLMs, retrieval-augmented generation (RAG), agents, classification, forecasting, and computer visionBuild proofs of concept and production pilots with Python and platforms such as Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI, Anthropic, or Hugging FaceWork with data engineers and developers to connect solutions to client systems, data pipelines, and APIsEvaluate models rigorously: accuracy, cost, latency, bias, and safety, with testing methods clients can trustGuide responsible AI practice: privacy (PIPEDA and provincial laws), governance, security, and human oversightTranslate technical tradeoffs into plain language for executives, and business needs into technical requirements for engineersRun workshops, demos, and training sessions that help client teams adopt AI and actually use itSupport change management so new tools stick after the project endsContribute to proposals, reusable assets, and internal research, and share what you learn with the teamFind your levelLevel Typical experience What you’ll ownAI Consultant I-0-2 years : Prototyping, data analysis, research, documentation, and client support, learning on live projects with a dedicated mentorAI Consultant II - 2-5 years : Owning workstreams end to end, leading solution design, running client workshops, managing day-to-day client relationships, mentoringSenior AI Consultant - 5+ years : Leading engagements, shaping AI strategy for clients, scoping and selling work, setting technical and ethical standards, developing the teamPromotion 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 bringMust haveSolid foundations in data and AI: Python, SQL, and an understanding of machine learning and modern LLM techniquesHands-on experience building something with AI, through a job, internship, coursework, or a serious personal projectClear communication: you can explain a model to a CFO and a business problem to an engineerCuriosity about how businesses work and a practical, results-first mindsetComfort with ambiguity, since client problems rarely arrive well definedDegree 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-tuningCloud experience on Azure, AWS, or GCP, and familiarity with MLOps and deployment practicesConsulting, client-facing, or customer-success experienceCertifications: Azure AI Engineer, AWS Machine Learning, Google Professional ML Engineer, or similarKnowledge of AI governance and risk frameworks (NIST AI RMF, ISO/IEC 42001) and Canadian privacy and AI regulationIndustry 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