Sanofi Pasteur Inc.

Sanofi Pasteur Inc.

Data and AI Engineer

Toronto, ON

Sponsorship not specified$101k-$151kDetected 4 days ago
AlgorithmsCode ReviewDatabricksVector DatabasesAWSAzureKafkaMachine LearningData EngineeringData ScienceLLMsRAGAgentic AILangGraphProduct ManagementProduct StrategyAgileSupply ChainHRFDA RegulatoryCollaborationGxP

About the role

  • Sanofi Digital M&S is on a bold journey to become the first AI-powered life sciences company - and the Data & AI Engineering team is at the forefront of this transformation.
  • This is not a role about maintaining the status quo - it's about reinventing it.
  • We're an R&D-driven, AI-powered biopharma company committed to improving people's lives and delivering compelling growth.

Responsibilities

  • Build data pipelines using industry frameworks.
  • Develop AI-powered decision-making systems that enable autonomous or semi-autonomous operations.
  • Build and maintain robust, scalable data pipelines that feed AI agents with real-time and historical data.
  • Design and implement RAG (Retrieval-Augmented Generation) architectures and vector database solutions to power knowledge-grounded AI agents.
  • Ensure data quality, reliability, and low-latency delivery to support production-grade agentic systems.
  • Partner closely with business stakeholders to deeply understand operational challenges and translate them into AI-powered solutions.
  • Collaborate with Data Scientists, Product Owners, IT Architects, and fellow engineers to deliver end-to-end AI products - not just models.
  • Participate in Agile ceremonies, code reviews, and engineering best practices to maintain a high-quality, high-velocity delivery culture.
  • Discover endless opportunities to grow your talent and drive your career, whether it's through a promotion or lateral move, at home or internationally.

Requirements

  • 3-5 years of hands-on experience in data engineering, AI/ML development, or software engineering.
  • Track record of delivering production-grade AI or data solutions with measurable business impact.

Nice to have

  • Experience with real-time data streaming and event-driven architectures (Kafka, Azure Event Hubs, etc.).
  • Knowledge of AI safety, responsible AI, and explainability practices.
  • Experience in the pharmaceutical or regulated industry, including GxP compliance frameworks (FDA 21 CFR Part 11, EU Annex 11).
  • Cloud or AI certifications (e.g., Azure AI Engineer, Databricks Certified, AWS ML Specialty).
  • Frontier AI Engineering: Work on cutting-edge agentic AI systems - multi-agent architectures, LLM integration, and real-time AI decision-making - with genuine operational impact at global scale.
  • Global Impact: Your AI solutions will directly influence the availability of life-saving medicines for patients worldwide.
  • Cutting-Edge Exposure: Stay at the forefront of AI - LangGraph, multi-agent systems, RAG, generative AI - in a team that actively explores and adopts emerging technologies.
  • Shape the Future: Contribute to the AI Engineering team's product roadmap and help define what AI-driven operations look like in a global pharma leader.

Compensation

  • 100,500.00 - 150,500.00 (Includes target bonus) Final compensation will be determined based on demonstrated experience, skills, location, and other relevant factors.

Benefits

  • Career Growth: Access to structured development programs, internal mobility, and continuous learning resources within Sanofi's digital ecosystem.
  • Competitive Package: Attractive compensation, performance bonuses, and comprehensive benefits.
  • Flexible Working: Hybrid work model supporting work-life balance.

Company info

  • Join a diverse, mission-driven team united by a passion for AI and operational excellence.

Visa & Work Authorization

  • ual opportunities to all regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, ability or gender identity
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This listing is sourced directly from Sanofi Pasteur Inc.'s careers page and normalized into a canonical job model.