Artificial Intelligence Engineer
- Toronto, ON, Canada
- Greater Toronto Area, Canada, Canada
$100 to $105 CAD / hour · Contract
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Job details
- Pay
- $100 to $105 CAD / hour
- Job type
- Contract
- Apply by
- Experience
- 6+ years of progressive experience in engineering roles, including at least 1-2 years leading emerging tech or AI initiatives.
- Posting language
- English
- Posted
Original job description
AI Engineer Responsibilities
- Lead and actively contribute to the development of AI products, pilots and solutions, with a focus on clean, maintainable code using Python, React, and AWS tools.
- Design, architect and build scalable Gen AI solutions, including LLM pipelines, Agentic, MCP, Graph/RAG architectures, and prompt-based applications and emerging tech.
- Implement cloud-native solutions using AWS services such as EKS, Lambda, Fargate, Glue, and Athena.
- Optimize performance of AI products, Drive continuous learning and experimentation with cutting-edge Gen AI methods, frameworks, APIs, and toolchains.
- Work closely with product managers, data scientists, and domain experts to define technical solutions aligned with business needs.
- Act as a subject matter expert (SME) on Gen AI technologies and help shape the organization's AI roadmap.
- Own end-to-end delivery of Gen AI solutions. Manage timelines, deliverables, and project milestones using Agile practices (Scrum/Kanban).
- Monitor operational metrics and incident data to drive continuous improvement and reliability.
- Ensure adherence to governance, DevSecOps protocols.
Experience/Skiils:
- 6+ years of progressive experience in engineering roles, including at least 1-2 years leading emerging tech or AI initiatives.
- Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering techniques
- Agentic AI, MCP, and Graph/RAG architectures
- Gen AI Framework (LangChain, LlamaIndex, Amazon Bedrock)
- Web application development using Next.js, React, TypeScript/JavaScript
- AWS cloud services (EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation)
- Infrastructure as Code (Puppet, Terraform, Docker) and containerized deployments
- ETL orchestration using Apache Airflow/DAGs
- Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune) and query optimization
- Python programming (NumPy, Pandas, Matplotlib, Boto3)
- Automated testing frameworks (Ragas, Playwright, Zephyr, Selenium,)
- Familiarity with SDLC best practices, DevSecOps, Agile Scrum/Kanban, and work management tools (JIRA, Confluence, JIRA Align).
- Knowledge of LLM fine tuning techniques
- Experience in BI tools like QuickSight, Tableau
- Knowledge of financial markets and enterprise data systems
More about this job
Responsibilities
Lead the design, development, and end-to-end delivery of scalable generative AI products and solutions, including LLM pipelines, agentic systems, and Graph/RAG applications using Python, React, and AWS. Collaborate with product, data science, and domain teams; guide AI strategy; and improve performance, reliability, governance, and delivery through experimentation and Agile practices.
Requirements
Requires 6+ years of engineering experience, including 1–2 years leading emerging technology or AI initiatives, with expertise in generative AI models, prompt engineering, agentic AI, Graph/RAG, and relevant frameworks. Candidates should also have experience with AWS, web development, infrastructure as code, ETL orchestration, vector or graph databases, Python, automated testing, DevSecOps, and Agile methods; financial markets and enterprise data knowledge are also noted.
Skills
- Python
- React
- TypeScript
- AWS
- Generative AI
- Large Language Models
- Prompt Engineering
- Agentic AI
- Model Context Protocol
- Graph/RAG Architectures
- LangChain
- LlamaIndex
- Amazon Bedrock
- Terraform
- Apache Airflow
- Vector and Graph Databases
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Data & Analytics
- Management & Leadership
Keywords
- Generative AI
- GPT
- Claude
- Gemini
- LLaMA
- Prompt Engineering
- Agentic AI
- Model Context Protocol
- Graph/RAG
- LangChain
- LlamaIndex
- Amazon Bedrock
- Python
- React
- Next.js
- TypeScript
- AWS
- EKS
- Lambda
- Fargate
- Glue
- Athena
- Terraform
- Docker
- Apache Airflow
- Weaviate
- Milvus
- PGVector
- Neo4j
- Agile Scrum/Kanban