AI Engineer: Production-Grade AI & Agentic Solutions

Myticas Consulting; Toronto; On-site; CAD 90,000 - 130,000; Full time

Job summary

Myticas Consulting is seeking an AI Engineer for a leading financial institution in Toronto. You will design, build, and deploy production-grade AI, ML, and Agentic AI solutions to address enterprise challenges.

Responsibilities include designing AI systems, building AI agents and RAG pipelines, developing AI/ML pipelines with LLMs/SLMs, and implementing MLOps, CI/CD, and cloud deployment best practices in a collaborative environment.

Qualifications

• Degree in Computer Science, Software Engineering, Data Science, or a related field.
• 3–5+ years of AI development experience, including at least 2 years building production AI/ML or Agentic AI solutions.
• Strong Python development skills.
• Experience building and deploying AI agents and AI/ML solutions.
• Experience with Docker, Kubernetes, CI/CD, and version control.
• Experience with API development and integration.
• Consulting or professional services experience preferred.

Responsibilities

• Design and implement production-grade AI and Agentic AI solutions.
• Build AI agents, agentic workflows, and Retrieval Augmented Generation (RAG) pipelines.
• Develop AI/ML pipelines using LLMs and SLMs.
• Implement MLOps, LLMOps, CI/CD, and cloud deployment best practices.
• Build AI governance, monitoring, observability, and evaluation frameworks.
• Contribute to AI standards, accelerators, and delivery best practices.

Skills

Python development

AI/ML development

Agentic AI

RAG pipelines

MLOps/LLMOps

Docker

Kubernetes

CI/CD

API development

Consulting experience

Education

Bachelor's degree in CS/SE/Data Science

Tools

Git

CI/CD tooling

API integration

Docker

Kubernetes

Job description

Myticas Consulting is seeking an AI Engineer for a leading financial institution in Toronto. You will design, build, and deploy production-grade AI, ML, and Agentic AI solutions to address enterprise challenges.

Responsibilities include designing AI systems, building AI agents and RAG pipelines, developing AI/ML pipelines with LLMs/SLMs, and implementing MLOps, CI/CD, and cloud deployment best practices in a collaborative environment.