Senior AI Engineer - Toronto, ON

Project Description

We are seeking an experienced AI Engineer to design and deliver scalable GenAI solutions. This subcontractor role focuses on building production‐ready AI products using Python, React, and AWS, while leveraging modern architectures such as LLM pipelines, RAG, and agent‐based systems.

Responsibilities

AI Engineering

• Lead and contribute to the development of AI products, pilots, and solutions, emphasizing clean, maintainable code in Python, React, and AWS.
• Design, architect, and build scalable GenAI systems, including LLM pipelines, agentic architectures, MCP, Graph/RAG, and prompt‐based applications.
• Implement cloud‐native solutions using AWS services such as EKS, Lambda, Fargate, Glue, and Athena.
• Optimize performance of AI products and drive continuous experimentation with emerging GenAI methods, frameworks, APIs, and toolchains.
• Collaborate with product managers, data scientists, and domain experts to define technical solutions aligned with business needs.
• Serve as a GenAI SME, helping shape the organization's AI roadmap.
• Own end‐to‐end delivery of GenAI solutions, managing timelines, deliverables, and milestones using Agile (Scrum/Kanban).
• Monitor operational metrics and incident data to support continuous improvement and reliability.
• Ensure adherence to governance, DevSecOps, and security protocols.

Required Skills & Experience

Must‐Have Qualifications

• 6+ years of progressive engineering experience, including 1–2 years leading emerging tech or AI initiatives.
• Hands‐on experience with GenAI models (GPT, Claude, Gemini, LLaMA) and prompt engineering.
• Expertise in agentic AI, MCP, and Graph/RAG architectures.
• Proficiency with GenAI frameworks: LangChain, LlamaIndex, Amazon Bedrock.
• Strong web development experience using Next.js, React, TypeScript/JavaScript.
• Deep knowledge of AWS services: EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation.
• Experience with IaC and containerization: Puppet, Terraform, Docker.
• ETL orchestration using Apache Airflow/DAGs.
• Familiarity with vector/graph databases: Weaviate, Milvus, PGVector, Neo4j, Neptune, including query optimization.
• Strong Python skills: NumPy, Pandas, Matplotlib, Boto3.
• Experience with automated testing frameworks: Ragas, Playwright, Zephyr, Selenium.
• Knowledge of SDLC best practices, DevSecOps, Agile (Scrum/Kanban), and work management tools ( JIRA, Confluence, JIRA Align).
• Understanding of LLM fine‐tuning techniques.
• Experience with BI tools: QuickSight, Tableau.
• Knowledge of financial markets and enterprise data systems.