AI Engineer
6-Month Contract with Potential Extension
Hybrid – 3 Days Onsite in Toronto
Compensation: Hourly Rates
T4: 70-75
IC: 83 - 90
You don't need extensive experience building AI agents to be successful in this role. Candidates with backgrounds in Data Science, Machine Learning, Quantitative Analysis, Mathematics, or Software Engineering who have a solid understanding of AI are encouraged to apply.
Key Responsibilities
- Partner with business and technical stakeholders to understand challenges, lead discovery discussions, and clarify requirements.
- Translate ambiguous business problems into structured problem statements, mathematical models, analytical frameworks, and technical solutions.
- Apply critical thinking, statistical reasoning, and quantitative techniques to investigate problems and evaluate potential approaches.
- Design and develop Python-based applications, prototypes, analytical models, and AI-enabled solutions.
- Identify opportunities to leverage generative AI, machine learning, large language models (LLMs), and agentic AI to address business challenges.
- Develop AI workflows incorporating APIs, tool calling, prompt engineering, context engineering, and workflow orchestration.
- Explore and implement Retrieval-Augmented Generation (RAG) solutions using embeddings, vector search, and contextual grounding.
- Analyze structured and unstructured data, test hypotheses, and evaluate model performance.
- Develop testing frameworks, evaluation datasets, and performance metrics to assess the accuracy and reliability of AI solutions.
- Conduct experimentation and error analysis to identify weaknesses and continuously improve AI capabilities.
- Communicate technical findings, recommendations, and potential limitations to technical and non-technical stakeholders.
Required Qualifications
- Strong analytical thinking, critical reasoning, and problem-solving abilities.
- Solid mathematical and quantitative foundations, including statistical reasoning and logical analysis.
- Professional experience in AI Engineering, Data Science, Machine Learning, Software Engineering, Quantitative Analysis, or a related technical discipline.
- Strong hands-on Python development experience.
- Understanding of artificial intelligence, machine learning, generative AI, and large language models (LLMs).
- Experience with data analysis, experimentation, hypothesis testing, and model evaluation.
- Ability to translate business requirements and ambiguous challenges into structured technical solutions.
- Strong communication and collaboration skills, with the ability to explain complex concepts to different audiences.
- Demonstrated ability to learn new technologies and adapt to unfamiliar technical challenges.
Preferred Qualifications
- Experience developing generative AI applications, AI agents, or LLM-powered solutions.
- Familiarity with Retrieval-Augmented Generation (RAG), embeddings, vector databases, and hybrid search.
- Experience with prompt engineering, tool calling, structured outputs, and AI workflow orchestration.
- Knowledge of Python libraries such as pandas, NumPy, scikit-learn, FastAPI, Pydantic, and pytest.
- Exposure to frameworks such as LangChain, LangGraph, or LlamaIndex.
- Experience with mathematical modelling, optimization, forecasting, or statistical analysis.
- Familiarity with automated testing, Git, CI/CD, Docker, and cloud-based application deployment.
- Previous experience in banking, financial services, or other regulated industries.
- Degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related quantitative discipline, or equivalent practical experience.
