Artificial Intelligence Engineer

Swoon; Toronto; Hybrid; CAD 96,000 - 103,000; Full time

Job summary

Swoon is seeking an AI/ML-focused engineer for a 6-month contract with potential extension. The role is hybrid, based in Toronto, with onsite 3 days a week and compensation paid hourly.

You will collaborate with stakeholders to translate business problems into mathematical models, develop Python-based AI solutions, and advance agentic AI capabilities. Strong analytical and communication skills are essential.

Qualifications

• Strong analytical thinking and quantitative reasoning for AI engineering and data science problems.
• Ability to translate business challenges into structured technical solutions.
• Experience with AI, ML, and large language models.
• Proven communication skills to explain complex concepts to varied audiences.
• Willingness to learn new technologies and adapt to evolving requirements.
• Experience collaborating with cross-functional teams.
• Demonstrated problem-solving in quantitative analysis and experimentation.
• Familiarity with Python data tools and ML libraries (Python, pandas, NumPy).

Responsibilities

• Partner with business and technical stakeholders to understand challenges and clarify requirements.
• Translate ambiguous business problems into structured statements, models, and technical solutions.
• Apply statistical reasoning to investigate problems and evaluate approaches.
• Design and develop Python-based applications, models, and AI-enabled solutions.
• Identify opportunities to leverage generative AI, LLMs, and agentic AI to address challenges.
• Develop AI workflows using APIs, tool calling, prompt engineering, and orchestration.
• Explore Retrieval-Augmented Generation with embeddings and vector search.
• Analyze data, test hypotheses, and evaluate model performance.
• Develop testing frameworks and evaluation datasets to measure accuracy.
• Conduct experiments and error analysis to improve AI capabilities.
• Communicate technical findings to both technical and non-technical stakeholders.

Skills

Analytical thinking

Statistical reasoning

AI/ML experience

Communication skills

Learning new technologies

Problem solving

Structured thinking

Team collaboration

Education

Degree in CS/DS/Math/Engineering

Equivalent practical experience

Tools

Python

LangChain

Pandas

NumPy

Job description

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.
• 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.