Applied AI Engineering Specialist – Hybride

Morgan Stanley

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.

Posted 7/3/2026full-timeMontreal • 🇨🇦 CanadaJuniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing GenAI systems, with a strong focus on LLMOps practices, data security, and AI-powered workflows. Proven ability to collaborate with cross-functional teams to enhance the adoption of GenAI capabilities across institutional applications.

Highest-signal resume keywords

GenAI System DevelopmentLLMOps ExpertisePython ProficiencyAI Document IngestionRetrieval System Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GenAI Workflow DesignLLM-Based System OperationEvaluation FrameworksPrompt ManagementRegression TestingCoding AgentsMulti-Stage PipelinesVector SearchMetadata FilteringDebugging and Stabilization

Industry Keywords

Institutional SecuritiesData SecurityPII HandlingAgentic WorkflowsOpen-Source ModelsProduction ReliabilityObservabilityQuality and Accuracy MetricsRecall/Precision TradeoffsNDCG

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

Comprehensive employee benefits and perks
Opportunities to move about the business