Prompt Engineer to design and optimize AI prompts and workflows using LLMs, Copilot, GenAI, and RAG architectures - JP1873

Location: Hybrid - Downtown Toronto - 2x/week in office The role is responsible for designing, optimizing, and operationalizing AI prompts and workflows that power decision-making across Capital Markets and Commercial Banking functions, including Trading, Corporate Banking, Credit Structuring, Research, Risk, and Operations. This role bridges front-office business needs and AI-driven insights, ensuring AI platforms deliver accurate, timely, and context-aware outputs aligned with market dynamics, regulatory requirements, and enterprise data. By developing a deep understanding of bankers’ day-to-day workflows, the Prompt Engineer creates curated, workflow-specific prompts and AI experiences that improve productivity, streamline processes, accelerate analysis, and enhance client service. The role also owns the design, governance, and continuous improvement of a centralized prompt library, providing reusable, scalable, and business-aligned prompt assets that drive consistent AI outcomes, promote best practices, and accelerate adoption across the organization. Must Haves

Solid experience (3+ years) in Capital Markets and Commercial Banking Sales, including but not limited to credit adjudication, AML/KYC, trading, investment products etc. Understanding of credit adjudication lifecycle, product offerings, pricing models, P&L;, risk metrics (VaR, sensitivities), and regulatory controls 1+ year of hands-on experience designing prompts for LLMs / Copilot / GenAI platforms Experience with RAG pipelines, vector search, embeddings, and agentic workflows Strong data analysis skills (structured + unstructured datasets) Familiarity with market data sources and financial datasets Ability to work closely with traders, bankers, risk managers, and technology teams Strong communication and translation across business and technology Nice to Have

Amazon Bedrock Agentcore experience Relevant postsecondary degree Market-Aware Analytical Thinking – Ability to interpret market movements and translate them into AI use cases Structured Problem Solving – Breaking down complex trading workflows into AI-driven components Business-to-Technology Translation – Converting front-office needs into scalable AI solutions Decision-Oriented Communication – Delivering clear, actionable AI outputs for time-sensitive decisions Continuous Learning & Adaptability – Keeping up with evolving AI capabilities and market changes Precision & Risk Awareness – Ensuring high accuracy in AI outputs in a high-stakes financial environment Responsibilities

AI Prompt Engineering for Capital Markets (Corporate Banking, Global Markets, Investment Banking) and Commercial Banking Design and optimize prompts for use cases across trading desks, sales workflows, research generation, pricing analytics, and risk reporting Tailor prompts for persona-specific needs (e.g., traders, sales, quants, risk managers) Business-to-AI Translation

Translate complex Capital Markets workflows (trade lifecycle, credit adjudication, pricing, P&L;, exposure) into effective AI-driven interactions Enable AI to generate insights on market events, positions,



client portfolios, and trade opportunities AI-Driven Decision Enablement

Ensure AI outputs are context-aware (market data, client context, regulatory constraints) and decision-ready Enable scenario analysis, trade recommendations, and risk insights using AI workflows Integration with Enterprise Data & Platforms

Leverage structured and unstructured data (market feeds, trade data, research, client notes) Integrate prompts with Microsoft Fabric, Graph, Dataverse, and trading/risk systems Support RAG-based architectures for research and knowledge retrieval Continuous Optimization & Performance Tuning

Iterate using feedback from traders, sales, and risk teams Improve precision, latency, and reliability of AI outputs Governance, Compliance & Responsible AI

Ensure AI outputs comply with Capital Markets and Commercial Banking regulations (e.g., trade surveillance, auditability, model risk) Embed guardrails for data privacy, explainability, and approval workflows Align with enterprise AI governance and model validation standards Adoption & Enablement

Develop reusable prompt libraries for trading, research, and sales workflows Train front-office and middle-office teams on effective AI usage Drive adoption of AI-assisted workflows across Capital Markets and Commercial Banking Duration: 6 months to start Location: Hybrid - Downtown Toronto - 2x/week in office The role is responsible for designing, optimizing, and operationalizing AI prompts and workflows that power decision-making across Capital Markets and Commercial Banking functions, including Trading, Corporate Banking, Credit Structuring, Research, Risk, and Operations. This role bridges front-office business needs and AI-driven insights, ensuring AI platforms deliver accurate, timely, and context-aware outputs aligned with market dynamics, regulatory requirements, and enterprise data. By developing a deep understanding of bankers’ day-to-day workflows, the Prompt Engineer creates curated, workflow-specific prompts and AI experiences that improve productivity, streamline processes, accelerate analysis, and enhance client service. The role also owns the design, governance, and continuous improvement of a centralized prompt library, providing reusable, scalable, and business-aligned prompt assets that drive consistent AI outcomes, promote best practices, and accelerate adoption across the organization. Must Haves

Strong experience (3+ years) in Capital Markets and Commercial Banking Sales, including but not limited to credit adjudication, AML/KYC, trading, investment products etc. Understanding of credit adjudication lifecycle, product offerings, pricing models, P&L;, risk metrics (VaR, sensitivities), and regulatory controls 1+ year of hands-on experience designing prompts for LLMs / Copilot / GenAI platforms Experience with RAG pipelines,



vector search, embeddings, and agentic workflows Strong data analysis skills (structured + unstructured datasets) Familiarity with market data sources and financial datasets Ability to work closely with traders, bankers, risk managers, and technology teams Strong communication and translation across business and technology Nice to Have

Microsoft ecosystem experience (Fabric, Power Platform, Graph API integration, etc) Amazon Bedrock Agentcore experience Relevant postsecondary degree Key Competencies

Market-Aware Analytical Thinking – Ability to interpret market movements and translate them into AI use cases Structured Problem Solving – Breaking down complex trading workflows into AI-driven components Business-to-Technology Translation – Converting front-office needs into scalable AI solutions Decision-Oriented Communication – Delivering clear, actionable AI outputs for time-sensitive decisions Continuous Learning & Adaptability – Keeping up with evolving AI capabilities and market changes Precision & Risk Awareness – Ensuring high accuracy in AI outputs in a high-stakes financial environment Responsibilities

AI Prompt Engineering for Capital Markets (Corporate Banking, Global Markets, Investment Banking) and Commercial Banking Design and optimize prompts for use cases across trading desks, sales workflows, research generation, pricing analytics, and risk reporting Tailor prompts for persona-specific needs (e.g., traders, sales, quants, risk managers) Business-to-AI Translation

Translate complex Capital Markets workflows (trade lifecycle, credit adjudication, pricing, P&L;, exposure) into effective AI-driven interactions Enable AI to generate insights on market events, positions, client portfolios, and trade opportunities AI-Driven Decision Enablement

Ensure AI outputs are context-aware (market data, client context, regulatory constraints) and decision-ready Enable scenario analysis, trade recommendations, and risk insights using AI workflows Integration with Enterprise Data & Platforms

Leverage structured and unstructured data (market feeds, trade data, research, client notes) Integrate prompts with Microsoft Fabric, Graph, Dataverse, and trading/risk systems Support RAG-based architectures for research and knowledge retrieval Continuous Optimization & Performance Tuning

Evaluate prompt performance using real trading scenarios Iterate using feedback from traders, sales, and risk teams Improve precision, latency, and reliability of AI outputs Governance, Compliance & Responsible AI

Ensure AI outputs comply with Capital Markets and Commercial Banking regulations (e.g., trade surveillance, auditability, model risk) Embed guardrails for data privacy, explainability, and approval workflows Align with enterprise AI governance and model validation standards Adoption & Enablement

Develop reusable prompt libraries for trading, research, and sales workflows Train front-office and middle-office teams on effective AI usage Drive adoption of AI-assisted workflows across Capital Markets and Commercial Banking Disclaimer: AI may be used in evaluating candidates. This posting is for an existing vacancy. #J-18808-Ljbffr

📌 Prompt Engineer to design and optimize AI prompts and workflows using LLMs, Copilot, GenAI, and RAG (Winnipeg)
🏢 S.i. Systems
📍 Winnipeg