2 hours ago
Toronto, CanadaMid Level
Responsibilities
- Design, develop, deploy, and support scalable Generative AI and Agentic AI solutions aligned with Sanofi’s strategic priorities.
- Architect and implement Retrieval-Augmented Generation solutions, multi-agent systems, and AI copilots using enterprise data and knowledge assets.
- Develop AI use cases including market research, competitive intelligence, omnichannel engagement, market access, conversational data interaction, and personalized patient support.
- Design reusable AI components, frameworks, libraries, and services that accelerate enterprise AI development.
- Build and optimize agentic workflows, prompt engineering strategies, orchestration frameworks, tool integrations, and evaluation processes.
- Develop and maintain production-ready code with testing, CI/CD, observability, documentation, scalability, security, and performance practices.
- Contribute to AI platform and enterprise data foundation strategies and continuously improve AI application quality, reliability, performance, and user adoption.
- Ensure AI solutions meet Responsible AI, security, privacy, governance, compliance, and regulatory requirements.
- Collaborate with product owners, business stakeholders, architects, data scientists, data engineers, and software engineers to deliver measurable business value.
- Communicate technical concepts and architectural decisions to technical and business audiences and mentor junior engineers, data scientists, and interns.
Requirements
- Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related quantitative discipline.
- At least 3 years of experience designing, building, and deploying AI/ML solutions in production environments.
- At least 2 years of hands-on experience developing GenAI, LLM, RAG, or Agentic AI applications.
- Strong programming and software engineering skills in Python, Java, or another enterprise programming language.
- Experience with SQL/NoSQL querying, Spark, REST APIs, Golang, NodeJS, Angular, and TypeScript.
- Experience with LangChain, LangGraph, Transformers, and vector database technologies.
- Strong understanding of agentic AI architectures, multi-agent systems, prompt engineering, tool or function calling, workflow orchestration, and AI application evaluation frameworks.
- Experience working with structured, semi-structured, and unstructured enterprise data, including Snowflake, Pinecone, AWS DocumentDB, S3, AWS RDS, or similar databases.
- Experience developing and deploying production-grade AI applications with emphasis on scalability, reliability, observability, security, and performance optimization.
- Understanding of AI/ML lifecycle management, including deployment, monitoring, evaluation, governance, and continuous improvement.
- Experience with AWS or GCP and cloud-native AI, data, and application services.
- Experience with Docker, Kubernetes, AWS EKS, AWS ECR, AWS EventBridge, CI/CD pipelines, and source control platforms such as GitHub or DevOps.
- Familiarity with SonarQube, Checkmarx, Artifactory, AWS CloudWatch, Datadog, JFrog, Grafana, Thanos, and Prometheus.
- Experience integrating AI solutions through REST APIs, microservices, and event-driven architectures.
- Knowledge of regulatory, compliance, and ethical considerations in AI and GenAI.
- Experience collaborating across engineering, product, business, data, and platform teams and defining technical architecture, dependencies, risks, and solutions.
- Experience mentoring junior AI engineers, data scientists, or technical team members.
- Strong written and verbal communication skills, including technical documentation, architecture reviews, and leadership presentations.
- AWS Certified ML Engineer or SnowPro Data Scientist certification is preferred.
- Experience in life sciences, healthcare, commercial operations, or other regulated industries is preferred.
- Experience with Agile/Scrum, Jira, and Confluence is preferred; fluency in English is required and additional languages are a plus.
Benefits
- Flexible working arrangement with up to 40% of time from home.
- International work environment with mentorship, training, and opportunities for professional development and internal or international mobility.
- Market-oriented salary and rewards package, with eligibility for employee benefits programs.
- Collective life and accident insurance.
- Health and wellbeing benefits, including healthcare, prevention, and wellness programs.
- Training and certification pathways through AWS, Snowflake, Informatica, and other providers.
- Structured onboarding, introductory training, and a dedicated buddy program.
Tech Stack
AngularApache SparkAWSDatadogDockerGoGoogle Cloud PlatformGrafanaInformaticaJavaKubernetesNode.jsPrometheusPythonSnowflakeSonarQubeSQLTypeScript
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