Senior Gen AI Engineer (LLMs, RAG)

About

Build next-generation AI solutions in the insurance industry using LLMs, RAG pipelines, vector databases, and Azure cloud. This hybrid Toronto role focuses on designing scalable GenAI systems, deploying production-ready AI services, and partnering with engineering and business teams to transform complex data into intelligent advisor tools.

What is in it for you:

• Salaried: $50-60 per hour.
• Incorporated Business Rate: $60-70 per hour.
• 8-month contract with the potential for permanent employment.
• Full-time position: 37.50 hours per week.
• Remote on Monday and Friday; on-site Tuesday to Thursday.

Responsibilities:

• Architect and develop LLM-based solutions including retrieval-augmented generation (RAG) pipelines, embeddings, model fine-tuning, and evaluation frameworks.
• Build scalable Generative AI microservices and integrate them with internal enterprise systems.
• Perform advanced prompt engineering, agent design, and implement safety guardrails for AI systems.
• Evaluate open-source and commercial language models based on performance, cost, and risk.
• Collaborate with data teams to prepare training datasets, knowledge bases, and analytics pipelines.
• Manage ingestion and refresh processes for knowledge bases supporting RAG architectures.
• Implement monitoring and feedback loops to continuously improve model performance and solution quality.
• Partner with business stakeholders to define problem statements, data requirements, and delivery approaches.
• Document solution architecture, data sources, and development standards.
• Present model performance, insights, and business impact to senior stakeholders.
• Contribute to business cases and support change-management considerations for solution adoption.
• Create architecture diagrams and technical documentation for engineering teams.
• Track tasks and progress using Jira in an agile project environment.
• Collaborate with cross-functional teams including data infrastructure, backend, and frontend engineering.
• Mentor junior team members and promote AI engineering best practices.
• Ensure compliance with enterprise security standards and insurance regulatory requirements.

What you will need to succeed:

• Bachelor’s degree in Computer Science, Mathematics, Engineering, or equivalent practical experience.
• 6+ years of experience in machine learning, natural language processing, or AI engineering.
• 2+ years of experience working with Generative AI and large language models.
• Hands-on experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, or Llama.
• Strong expertise in retrieval-augmented generation (RAG), vector databases, embeddings, and model evaluation methods.
• Proficiency in Python and experience building data pipelines.
• Experience designing and deploying cloud-native architectures, preferably on Microsoft Azure.
• Proven experience deploying Generative AI solutions in production environments with monitoring and operational controls.
• Strong SQL and data modeling skills.
• Familiarity with relational and NoSQL databases or distributed data environments.
• Familiarity with BI or visualization tools such as Power BI or Tableau is considered an asset.
• Knowledge of classical machine learning or statistical methods such as regression, clustering, or tree-based models.
• Ability to translate technical findings into business insights and communicate with non-technical stakeholders.
• Strong problem-solving, collaboration, and communication skills.
• Experience in insurance, financial services, or regulated industries is considered an asset.