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AI Engineer – Snowflake
LinkedIn Recruiter Post
Senior AI Engineer with Snowflake expertise needed to design and productionize AI/ML and GenAI solutions. Hybrid in Toronto, contract role.
About the role
- Applied AI Engineering Specialist at Morgan Stanley designing and scaling GenAI platforms for Institutional Securities applications. Developing AI-powered assistants and guiding GenAI architecture decisions.
Responsibilities
- Design and evolve reusable GenAI workflow primitives and services used across Institutional Securities workflows
- Develop AI-powered assistants embedded into core Institutional Securities applications, leveraging agentic and tool-driven workflows
- Define and guide GenAI architecture decisions, including model selection, orchestration patterns, and evaluation strategies
- Establish and evolve LLMOps practices, including evaluation harnesses, prompt/version management, monitoring, and regression testing
- Design and implement controls for entitlements, data security, and PII handling, including usage of open-source models in regulated environments
- Partner with business and platform teams to drive adoption of shared GenAI capabilities across systems and workflows
Requirements
- At least 1 year of hands-on experience building and operating GenAI systems in production
- At least 6+ years of full-stack or platform engineering experience, with strong proficiency in Python
- Proven experience designing and operating LLM-based systems using patterns such as RAG, tool/function calling, agentic workflows, and structured outputs
- Strong expertise in LLMOps, including evaluation frameworks, prompt/version management, regression testing, observability, and production reliability
- Experience building AI-first document ingestion and extraction pipelines with measurable quality and accuracy
- Experience with coding agents (Claude code, Codex, AMP, CoPilot)
- Advanced experience in retrieval systems, including multi-stage pipelines, vector search, re-ranking, metadata filtering, and evaluation metrics (e.g., recall/precision tradeoffs, MRR, NDCG)
- Practical experience debugging and stabilizing systems through real-world failure scenarios, including model regressions, prompt drift, retrieval degradation, and data quality issues.
Benefits
- Comprehensive employee benefits and perks
- Opportunities to move about the business
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