Senior AI Engineer (Permanent)

Zohorecruit; Toronto; Hybrid; CAD 120,000 - 180,000; Full time

Job summary

CoreFactor is seeking a Senior AI Engineer for a permanent/full-time role in Toronto. The position focuses on applied Generative AI and Agentic AI, leveraging existing LLMs via API integration to deliver production-grade solutions.

You will own the design and implementation of intelligent applications, mentor junior engineers, and collaborate with business, governance, and engineering teams to scale AI capabilities.

Qualifications

• ,

Responsibilities

• Architect and build production-grade intelligent systems and applications using existing LLMs 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 complex business workflows.
• Contribute to the design of a reusable, scalable AI platform with shared tooling, agent frameworks, and infrastructure.
• Build web app AI solutions using frameworks such as React or Streamlit.

Skills

Prompt engineering

RAG

Fine-tuning

GenAI evaluation

Python

ML libraries

Cloud platforms

Communication

Education

Degree in CS/AI/DS/ML/STATS

Tools

OpenAI API

React

Streamlit

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 seekingan experienced and technically strongSenior AI Engineerto join our clients Data, AI & Analytics team. This role focuses on theapplied use of Generative AI and Agentic AI,leveragingexisting 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'llbe building the agents and GenAI systems that CI's advisors, analysts, and operations teamsactually relyon 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 tooluseto 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 orStreamlit.

Monitoring & Improvement

• Set technical direction and best practices for GenAI and agentic solution design, including evaluationmethodology, 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.

Technical Leadership & Best Practices

• Lead design and code reviews, championing best practices around experimentation, reproducibility, and responsible AI.
  

Requirements

Education

• Bachelor's orMaster's degree in Computer Science, Data Science, AI, Machine Learning, Statistics, or a related field.
  

Experience

• 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.
  

Skills

• Deep understanding of prompt engineering, RAG, fine-tuning, and evaluation of generative output, includingtradeoffsacross approaches.
  
• Proficiencyin 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).