Senior AI Engineer (Permanent)
- Toronto, ON, Canada
- Greater Toronto Area, Canada, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Experience
- 4–8+ years of experience in a data science, machine learning, or AI engineering role, including hands-on experience delivering production systems.
- Posting language
- English
- Posted
Original job description
CoreFactor is searching for a Senior AI Engineer on a permanent/full-time basis.
This position is hybrid and will require the successful incumbent to go into the office four (4) times per week.
We are seeking an experienced and technically strong Senior AI Engineer to join our clients Data, AI & Analytics team. This role focuses on the applied use of Generative AI and Agentic AI, leveraging existing large language models (LLMs) and platforms - not building models from scratch. As a senior member of the team, you'll take technical ownership of intelligent applications and automation tools that use LLMs to solve business problems, set best practices for the team, and mentor junior engineers, while partnering closely with business, engineering, and governance stakeholders to bring production-grade AI solutions to life.
KEY RESPONSIBILITIES:
Applied Generative & Agentic AI:
You'll be building the agents and GenAI systems that CI's advisors, analysts, and operations teams actually rely on day to day - not prototypes that stall out after the demo.
Education:
This position is hybrid and will require the successful incumbent to go into the office four (4) times per week.
We are seeking an experienced and technically strong Senior AI Engineer to join our clients Data, AI & Analytics team. This role focuses on the applied use of Generative AI and Agentic AI, leveraging existing large language models (LLMs) and platforms - not building models from scratch. As a senior member of the team, you'll take technical ownership of intelligent applications and automation tools that use LLMs to solve business problems, set best practices for the team, and mentor junior engineers, while partnering closely with business, engineering, and governance stakeholders to bring production-grade AI solutions to life.
KEY RESPONSIBILITIES:
Applied Generative & Agentic AI:
You'll be building the agents and GenAI systems that CI's advisors, analysts, and operations teams actually rely on day to day - not prototypes that stall out after the demo.
- Architect and build production-grade intelligent systems and applications using existing LLMs (e.g., OpenAI, Claude, Gemini, etc.) through API integration, prompt engineering, fine-tuning, or Retrieval-Augmented Generation (RAG).
- Design, prototype, and lead development of agentic AI systems capable of multi-step reasoning, task decomposition, and tool use to automate and support complex business workflows.
- Contribute to the design of a reusable, scalable AI platform (shared tooling, agent frameworks, and infrastructure) rather than one-off point solutions, so capabilities can be reused across use cases.
- Building web app AI solutions using frameworks such as React or Streamlit.
- Set technical direction and best practices for GenAI and agentic solution design, including evaluation methodology, guardrails, and reliability of LLM-powered systems.
- Drive continuous iteration and improvement of models and LLM-powered tools based on performance metrics, cost, and user feedback.
- Lead design and code reviews, championing best practices around experimentation, reproducibility, and responsible AI.
Education:
- Bachelor's or Master's degree in Computer Science, Data Science, AI, Machine Learning, Statistics, or a related field.
- 4–8+ years of experience in a data science, machine learning, or AI engineering role, including hands-on experience delivering production systems.
- Demonstrated experience designing, building, and shipping agentic AI systems involving multi-step reasoning, task decomposition, and tool use.
- Experience designing around real-world infrastructure limitations - such as rate limits, quota management, retries/backoff, and cost-per-call - when architecting LLM-powered systems at scale.
- Deep understanding of prompt engineering, RAG, fine-tuning, and evaluation of generative output, including tradeoffs across approaches.
- Proficiency in Python and ML/AI libraries (e.g., Scikit-learn, PyTorch, TensorFlow).
- Hands-on skills in designing and orchestrating agentic workflows (multi-step reasoning, task decomposition, tool/function calling).
- Excellent communication skills; able to present complex technical concepts clearly to both technical and non-technical stakeholders, including senior leadership.
- Experience with cloud-based environments (AWS, Azure, or GCP).
More about this job
Responsibilities
Design, build, and maintain production-grade generative and agentic AI applications using existing LLMs, including reusable platforms and tools that automate business workflows. Set technical best practices, evaluate and improve system performance and reliability, and lead design and code reviews while mentoring junior engineers and collaborating with stakeholders.
Requirements
Requires a bachelor's or master's degree in a relevant field and 4–8+ years of experience delivering production data science, machine learning, or AI systems. Candidates should have hands-on experience building agentic workflows and LLM applications, strong Python and AI/ML skills, knowledge of infrastructure constraints and cloud environments, and the ability to communicate effectively with technical and non-technical stakeholders.
Skills
- Generative AI
- Agentic AI
- Large Language Models
- Prompt Engineering
- Retrieval-Augmented Generation
- Fine-Tuning
- Python
- Scikit-Learn
- PyTorch
- TensorFlow
- Agent Orchestration
- Tool and Function Calling
- AI Evaluation
- Cloud Computing
- React
- Streamlit
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Data & Analytics
- Software
- Engineering
Keywords
- Generative AI
- Agentic AI
- Large Language Models
- OpenAI
- Claude
- Gemini
- Prompt Engineering
- Retrieval-Augmented Generation
- Fine-Tuning
- Multi-Step Reasoning
- Task Decomposition
- Tool Use
- API Integration
- AI Agents
- AI Platform
- Python
- Scikit-Learn
- PyTorch
- TensorFlow
- React
- Streamlit
- Evaluation Methodology
- Responsible AI
- Guardrails
- Rate Limits
- Quota Management
- Retries and Backoff
- Cost Management
- AWS
- Azure