Senior Analyst, AI
$61,000–$92,200 year
Hybrid · Toronto, Ontario, Canada
Job Summary
Senior AI-focused data & analytics professional to design, build, and optimize vector-based AI/LLM workflows for healthcare data at UHN. Design, implement, and maintain embedding pipelines and vector storage; enable semantic search, RAG architectures, and tool orchestration; integrate with Azure AI tools and Pinecone/Qdrant; drive scalable AI deployment pipelines, governance, and responsible-AI practices; collaborate across neuroscience, cardiology, oncology, and other regulated domains with a strong emphasis on production-ready ML/AI solutions and privacy.
Required Qualifications
- Bachelor degree in Computer Science, Data Science, Engineering, Mathematics, or a related field
- 3–5 years of hands-on experience delivering AI, machine learning, or data science solutions in production
- Strong programming skills in Python and SQL
- Experience with machine learning frameworks such as scikit-learn or XGBoost
- Experience with Generative AI technologies including LLMs, prompt engineering, RAG pipelines, and embeddings
- Experience building vector databases, semantic search systems, and retrieval architectures
- Experience with vector databases such as Azure AI Search, Qdrant, or Pinecone
- Experience with Azure Machine Learning and Azure AI Foundry
- Proficiency in scalable AI workflows, APIs, deployment pipelines, and operationalization
- Familiarity with responsible AI, governance, privacy, and regulated environments
- Experience with LangChain, Semantic Kernel, or agentic AI frameworks an asset
- Familiarity with MLOps / LLMOps and CI/CD practices an asset
- Experience in healthcare, research, or other regulated industries an asset
- Exposure to enterprise AI platform design and AI enablement initiatives an asset
Desired Qualifications
- Bachelor degree in Computer Science, Data Science, Engineering, Mathematics, or a related field
- 3–5 years of hands-on experience delivering AI, machine learning, or data science solutions in production
- Strong programming skills in Python and SQL
- Experience with machine learning frameworks such as scikit-learn or XGBoost
- Experience with Generative AI technologies including LLMs, prompt engineering, RAG pipelines, and embeddings
- Experience building vector databases, semantic search systems, and retrieval architectures
- Experience with vector databases such as Azure AI Search, Qdrant, or Pinecone
- Experience with Azure Machine Learning and Azure AI Foundry
- Proficiency in scalable AI workflows, APIs, deployment pipelines, and operationalization
- Familiarity with responsible AI, governance, privacy, and regulated environments
- Experience with LangChain, Semantic Kernel, or agentic AI frameworks an asset
- Familiarity with MLOps / LLMOps and CI/CD practices an asset
- Experience in healthcare, research, or other regulated industries an asset
- Exposure to enterprise AI platform design and AI enablement initiatives an asset
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