Senior Gen AI Engineer

Recrute Action•Toronto, ON

1d•Hybrid

About The Position

Exciting opportunity in the insurance sector to work on cutting-edge AI solutions powered by LLMs and RAG pipelines. This hybrid Toronto-based role offers hands-on innovation, real business impact, and collaboration on high-visibility projects shaping the future of intelligent systems.

Requirements

Bachelor’s degree in Computer Science, Mathematics, Engineering, or equivalent practical experience.
6+ years of experience in machine learning, NLP, or AI engineering.
2+ years of hands-on experience with GenAI and LLM technologies.
Experience with LLM platforms such as OpenAI, Azure OpenAI, Anthropic, or Llama.
Strong expertise in RAG, vector databases, embeddings, and model evaluation methods.
Proficiency in Python, data pipelines, and cloud-native architectures, preferably Azure.
Experience deploying GenAI solutions in production environments.
Strong SQL and data modeling skills, with familiarity in relational and NoSQL databases.
Understanding of classical machine learning and statistical methods.
Ability to translate technical results into business insights and support stakeholder decision-making.
Strong problem-solving, communication, and cross-functional collaboration skills.

Nice To Haves

Knowledge of BI and visualization tools such as Power BI or Tableau is an asset.
Experience in insurance, underwriting, or financial services is an asset.

Responsibilities

Design and architect LLM-based solutions, including RAG pipelines, fine-tuning, embeddings, and evaluation frameworks.
Build scalable GenAI microservices and integrate them with internal systems.
Perform prompt engineering, agent design, and implement safety guardrails.
Evaluate open-source and commercial models for performance, risk, and cost.
Collaborate with data teams to prepare training datasets, knowledge bases, and analytics.
Ensure compliance with insurance regulations and enterprise security standards.
Partner with stakeholders to define problem statements, data needs, and delivery approaches.
Document data sources and maintain structured templates.
Oversee ingestion and refresh of knowledge bases and data pipelines supporting RAG solutions.
Implement quality controls and continuous improvement mechanisms.
Present model performance, insights, and business impact to senior stakeholders.
Develop business cases and support change management for solution adoption.
Work with internal and external teams to productionize solutions.
Mentor junior team members and contribute to best practices.
Participate in daily project updates and communicate with business and technical partners.
Propose and implement technical solutions aligned with timelines and requirements.
Develop proof-of-concept solutions and architecture diagrams.
Track tasks and progress using Jira.